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• 1. Introduction
• 2. Classifiers based on Bayes Decision
• 3. Linear Classifiers
• 4. Nonlinear Classifiers
• 5. Feature Selection
• 6. Feature Generation I: Data Transformation and Dimensionality Reduction
• 7. Feature Generation II
• 8. Template Matching
• 9. Context Depedant Clarification
• 10. System Evaultion
• 11. Clustering: Basic Concepts
• 12. Clustering Algorithms: Algorithms L Sequential
• 13. Clustering Algorithms II: Hierarchical
• 14. Clustering Algorithms III: Based on Function Optimization
• 15. Clustering Algorithms IV: Clustering
• 16. Cluster Validity.
• (source: Nielsen Book Data)

This book considers classical and current theory and practice, of supervised, unsupervised and semi-supervised pattern recognition, to build a complete background for professionals and students of engineering. The authors, leading experts in the field of pattern recognition, have provided an up-to-date, self-contained volume encapsulating this wide spectrum of information. The very latest methods are incorporated in this edition: semi-supervised learning, combining clustering algorithms, and relevance feedback. * Thoroughly developed to include many more worked examples to give greater understanding of the various methods and techniques * Many more diagrams included--now in two color--to provide greater insight through visual presentation * Matlab code of the most common methods are given at the end of each chapter. * More Matlab code is available, together with an accompanying manual, via this site * Latest hot topics included to further the reference value of the text including non-linear dimensionality reduction techniques, relevance feedback, semi-supervised learning, spectral clustering, combining clustering algorithms. * An accompanying book with Matlab code of the most common methods and algorithms in the book, together with a descriptive summary, and solved examples including real-life data sets in imaging, and audio recognition. The companion book will be available separately or at a special packaged price (ISBN: 9780123744869).
(source: Nielsen Book Data)

• Foreword. Correcting the Training Data
• R. Barandela, et al. Context Free Grammars and Semantic Networks for Flexible Assembly Recognition
• C. Bauckhage, G. Sagerer. Stochastic Recognition of Occluded Objects
• B. Bhanu, et al. Approximate String Matching for Angular String Elements with Applications to On-Line and Off-line Handwriting Recognition
• S.-H. Cha, S.N. Srihari. Uniform, Fast Convergence of Arbitrarily Tight Upper and Lower Bounds on the Bayes Error
• D. Chen, et al. Building RBF Networks for Time Series Classification by Boosting
• J.R. Diez, C.J.A. Gonzalez. Similarity Measures and Clustering of String Patterns
• A. Fred. Pattern Recognition for Intrusion Detection in Computer Networks
• G. Giacinto, F. Roli. Model-Based Pattern Recognition
• M. Haindl. Structural Pattern Recognition in Graphs
• L. Holder, et al. Deriving Pseudo-Probabilities of Correctness Given Scores (DPPS)
• K. Ianakiev, V. Govindaraju. Weighed Mean and Generalized Median of Strings
• Y. Jiang, H. Bunke. A Region-Based Algorithm for Classifier-Independent Feature Selection
• M. Kudo. Inference of K-Piecewise Testable Tree Languages
• D. Lopez, et al. Mining Partially Periodic Patterns With Unknown Periods From Event tream
• S. Ma, J.L. Hellerstein. Combination of Classifiers for Supervised Learning: A Survey
• S. Ma, C. Ji. Image Segmentation and Pattern Recognition: A Novel Concept, the Historgram of Connected Elements
• D. Maravell, M.A. Patricio. Prototype Extraction for k-NN Classifiers using Median Srings
• C.D. Martinez-Hinarejos, et al. Cyclic String Matching: Efficient Exact and Approximate Algorithms
• A. Marzal, et al. Homogeneity, Autocorrelation and Anisotropy in Patterns
• A. Molina. Robust Structural Indexing through Quasi-Invariant Shape Signatures and FeatureGeneration
• H. Nishida. Energy Minimisation Methods for Static and Dynamic Curve Matching
• E. Nyssen, et al. Recent Feature Selection Methods in Statistical Pattern Recognition
• P. Pudil, et al. Fast Image Segmentation under Noise
• R.M. Romano, D. Vitulano. Set Analysis of Coincident Errors and Its Applications for Combining Classifiers
• D. Ruta, B. Gabrys. Enhanced Neighbourhood Specifications for Pattern Classification
• J.S. San--nchez, A.I. Marques. Algorithmic Synthesis in Neural Network Training for Pattern Recognition
• K. Sirlantzis. Binary Strings and multi-class learning problems
• T. Windeatt, R. Ghaderi.
• (source: Nielsen Book Data)

The research and development of pattern recognition have proven to be of importance in science, technology, and human activity. Many useful concepts and tools from different disciplines have been employed in pattern recognition. Among them is string matching, which receives much theoretical and practical attention. String matching is also an important topic in combinatorial optimization. This book is devoted to recent advances in pattern recognition and string matching. It consists of twenty eight chapters written by different authors, addressing a broad range of topics such as those from classifica- tion, matching, mining, feature selection, and applications. Each chapter is self-contained, and presents either novel methodological approaches or applications of existing theories and techniques. The aim, intent, and motivation for publishing this book is to pro- vide a reference tool for the increasing number of readers who depend upon pattern recognition or string matching in some way. This includes students and professionals in computer science, mathematics, statistics, and electrical engineering. We wish to thank all the authors for their valuable efforts, which made this book a reality. Thanks also go to all reviewers who gave generously of their time and expertise.
(source: Nielsen Book Data)

• An improved pattern matching algorithm for strings in terms of straight-line programs.- Episode matching.- Efficient algorithms for approximate string matching with swaps.- On the complexity of pattern matching for highly compressed two-dimensional texts.- Estimating the probability of approximate matches.- Space- and time-efficient decoding with canonical huffman trees.- On weak circular squares in binary words.- An easy case of sorting by reversals.- External inverse pattern matching.- Distributed generation of suffix arrays.- Direct construction of compact directed acyclic word graphs.- Approximation algorithms for the fixed-topology phylogenetic number problem.- A new algorithm for the ordered tree inclusion problem.- On incremental computation of transitive closure and greedy alignment.- Aligning coding DNA in the presence of frame-shift errors.- A filter method for the weighted local similarity search problem.- Trie-based data structures for sequence assembly.- Flexible identification of structural objects in nucleic acid sequences: Palindromes, mirror repeats, pseudoknots and triple helices.- Banishing bias from consensus sequences.- On the Nadeau-Taylor theory of conserved chromosome segments.- Iterative versus simultaneous multiple sequence alignment.- Modern comparative lexicostatistics.
• (source: Nielsen Book Data)

This book constitutes the refereed proceedings of the Eighth Annual Symposium on Combinatorial Pattern Matching, CPM 97, held in Aarhus, Denmark, in June/July 1997. The volume presents 20 revised full papers carefully selected from 32 submissions received; also included are abstracts of two invited contributions. The volume is devoted to the issue of searching and matching strings and more complicated patterns, such as trees, regular expressions, graphs, point sets and arrays. The results presented are particularly relevant to molecular biology, but also to information retrieval, pattern recognition, compiling, data compression and program analysis.
(source: Nielsen Book Data)

• 1. Introduction
• 2. Classifiers based on Bayes Decision
• 3. Linear Classifiers
• 4. Nonlinear Classifiers
• 5. Feature Selection
• 6. Feature Generation I: Data Transformation and Dimensionality Reduction
• 7. Feature Generation II
• 8. Template Matching
• 9. Context Depedant Clarification
• 10. System Evaultion
• 11. Clustering: Basic Concepts
• 12. Clustering Algorithms: Algorithms L Sequential
• 13. Clustering Algorithms II: Hierarchical
• 14. Clustering Algorithms III: Based on Function Optimization
• 15. Clustering Algorithms IV: Clustering
• 16. Cluster Validity.
• (source: Nielsen Book Data)

This book considers classical and current theory and practice, of supervised, unsupervised and semi-supervised pattern recognition, to build a complete background for professionals and students of engineering. The authors, leading experts in the field of pattern recognition, have provided an up-to-date, self-contained volume encapsulating this wide spectrum of information. The very latest methods are incorporated in this edition: semi-supervised learning, combining clustering algorithms, and relevance feedback. * Thoroughly developed to include many more worked examples to give greater understanding of the various methods and techniques * Many more diagrams included--now in two color--to provide greater insight through visual presentation * Matlab code of the most common methods are given at the end of each chapter. * More Matlab code is available, together with an accompanying manual, via this site * Latest hot topics included to further the reference value of the text including non-linear dimensionality reduction techniques, relevance feedback, semi-supervised learning, spectral clustering, combining clustering algorithms. * An accompanying book with Matlab code of the most common methods and algorithms in the book, together with a descriptive summary, and solved examples including real-life data sets in imaging, and audio recognition. The companion book will be available separately or at a special packaged price (ISBN: 9780123744869).
(source: Nielsen Book Data)

• A fast bit-vector algorithm for approximate string matching based on dynamic programming / G. Myers
• A bit-parallel approach to suffix automata : fast extended string matching / G. Navarro and M. Raffinot
• A dictionary matching algorithm fast on the average for terms of varying lengths / M. Ziv-Ukelson and A. Kershenbaum
• A very fast string matching algorithm for small alphabets and long patterns / C. Charras, T. Lecroq and J.D. Pehoushek
• Approximate word sequence matching over spase suffix trees / K.M. Risvik
• Efficient parallel algorithm for the editing distance between ordered trees / K. Zhang
• Reporting exact and approximate regular expression matches / E.G. Myers, P. Oliva and K. Guimarães
• An approximate oracle for distance in metric spaces / Y. Yang [and others]
• A rotation invariant filter for two-dimensional string matching / K. Frederiksson and E. Ukkonen
• Constructing suffix arrays for multi-dimensional matrices / D.K. Kim, Y.A. Kim and K. Park
• Simple and flexible detection of contiguous repates using a suffix tree / J. Stoye and D. Gusfield
• Comparison and coding DNA / C.N.S. Pederson, R. Lyngs and J. Hein
• Fixed topology alignment with recombination / B. Ma, L. Wang and M. Li
• Aligning alignments / J.D. Kececioglu and W. Zhang
• Efficient special cases of pattern matching with swaps / A. Amir [and others]
• Aligning DNA sequences to minimize the changes in protein / Y. Hua, T. Jiang and B. Wu
• Genome halving / N. El-Mabrouk, J.H. Nadeau and D. Sankoff.

This book constitutes the refereed proceedings of the 9th Annual Symposium on Combinatorial Pattern Matching, CPM 98, held in Piscataway, NJ, USA, in July 1998. The 17 revised full papers presented were carefully reviewed and selected for inclusion in the book. The papers address all current issues in combinatorial pattern matching dealing with a variety of classical objects to be matched as well as with DNA coding.

• 1. Introduction
• 2. Classifiers based on Bayes Decision
• 3. Linear Classifiers
• 4. Nonlinear Classifiers
• 5. Feature Selection
• 6. Feature Generation I: Data Transformation and Dimensionality Reduction
• 7. Feature Generation II
• 8. Template Matching
• 9. Context Depedant Clarification
• 10. System Evaultion
• 11. Clustering: Basic Concepts
• 12. Clustering Algorithms: Algorithms L Sequential
• 13. Clustering Algorithms II: Hierarchical
• 14. Clustering Algorithms III: Based on Function Optimization
• 15. Clustering Algorithms IV: Clustering
• 16. Cluster Validity.
• (source: Nielsen Book Data)

This book considers classical and current theory and practice, of supervised, unsupervised and semi-supervised pattern recognition, to build a complete background for professionals and students of engineering. The authors, leading experts in the field of pattern recognition, have provided an up-to-date, self-contained volume encapsulating this wide spectrum of information. The very latest methods are incorporated in this edition: semi-supervised learning, combining clustering algorithms, and relevance feedback. * Thoroughly developed to include many more worked examples to give greater understanding of the various methods and techniques * Many more diagrams included--now in two color--to provide greater insight through visual presentation * Matlab code of the most common methods are given at the end of each chapter. * More Matlab code is available, together with an accompanying manual, via this site * Latest hot topics included to further the reference value of the text including non-linear dimensionality reduction techniques, relevance feedback, semi-supervised learning, spectral clustering, combining clustering algorithms. * An accompanying book with Matlab code of the most common methods and algorithms in the book, together with a descriptive summary, and solved examples including real-life data sets in imaging, and audio recognition. The companion book will be available separately or at a special packaged price (ISBN: 9780123744869).
(source: Nielsen Book Data)

• A linear time pattern matching algorithm between a string and a tree
• Tight comparison bounds for the string prefix-matching problem
• 3-D docking of protein molecules
• Minimal separators of two words
• Covering a string
• On the worst-case behaviour of some approximation algorithms for the shortest common supersequence of k strings
• An algorithm for locating non-overlapping regions of maximum alignment score
• Exact and approximation algorithms for the inversion distance between two chromosomes
• The maximum weight trace problem in multiple sequence alignment
• An algorithm for approximate tandem repeats
• Two dimensional pattern matching in a digitized image
• Analysis of a string edit problem in a probabilistic framework
• Detecting false matches in string matching algorithms
• On suboptimal alignments of biological sequences
• A fast filtration algorithm for the substring matching problem
• A unifying look at d-dimensional periodicities and space coverings
• Approximate string-matching over suffix trees
• Multiple sequence comparison and n-dimensional image reconstruction
• A new editing based distance between unordered labeled trees.

The papers contained in this volume were presented at the Fourth Annual Symposium on Combinatorial Pattern Matching, held in Padova, Italy, in June 1993. Combinatorial pattern matching addresses issues of searching and matching of strings and more complicated patterns such as trees, regular expressions, extended expressions, etc. The goal is to derive nontrivial combinatorial properties for such structures and then to exploit these properties in order to achieve superior performance for the corresponding computational problems. In recent years, a steady flow of high-quality scientific studies of this subject has changed a sparse set of isolated results into a full-fledged area of algorithmics. The area is expected to grow even further due to the increasing demand for speedand efficiency that comes especially from molecular biology and the Genome project, but also from other diverse areas such as information retrieval, pattern recognition, compilers, data compression, and program analysis.

• Probabilistic analysis of generalized suffix trees
• A language approach to string searching evaluation
• Pattern matching with mismatches: A probabilistic analysis and a randomized algorithm
• Fast multiple keyword searching
• Heaviest increasing/common subsequence problems
• Approximate regular expression pattern matching with concave gap penalties
• Matrix longest common subsequence problem, duality and hilbert bases
• From regular expressions to DFA's using compressed NFA's
• Identifying periodic occurrences of a template with applications to protein structure
• Edit distance for genome comparison based on non-local operations
• 3-D substructure matching in protein Molecules
• Fast serial and parallel algorithms for approximate tree matching with VLDC's (Extended Abstract)
• Grammatical tree matching
• Theoretical and empirical comparisons of approximate string matching algorithms
• Fast and practical approximate string matching
• DZ A text compression algorithm for natural languages
• Multiple alignment with guaranteed error bounds and communication cost
• Two algorithms for the longest common subsequence of three (or more) strings
• Color Set Size problem with applications to string matching
• Computing display conflicts in string and circular string visualization
• Efficient randomized dictionary matching algorithms
• Dynamic dictionary matching with failure functions.

This volume contains the 22 papers accepted for presentation at the Third Annual Symposium on Combinatorial Pattern Matching held April 29 to May 1, 1992, in Tucson, Arizona; it constitutes the first conference proceedings entirely devoted to combinatorial pattern matching (CPM). CPM deals withissues of searching and matching of strings and other more complicated patterns such as trees, regular expressions, extended expressions, etc. in order to derive combinatorial properties for such structures. As an interdisciplinary field of growing interest, CPM is related to research in information retrieval, pattern recognition, compilers, data compression, and program analysis as well as to results, problems and methods from combinatorial mathematics and molecular biology.

• Shift-and approach to pattern matching in LZW compressed text / Takuya Kida [and others]
• A general practical approach to pattern matching over Ziv-Lempel compressed text / Gonzalo Navarro and Mathieu Raffinot
• Pattern matching in text compressed by using antidictionaries / Yusuke Shibata [and others]
• On the structure of syntenic distance / David Liben-Nowell
• Physical mapping with repeated probes: the hypergraph superstring problem / Serafim Batzoglou and Sorin Istrail
• Hybridization and genome rearrangement / Nadia El-Mabrouk and David Sankoff
• On the complexity of positional sequencing by hybridization / Amir Ben-Dor [and others]
• GESTALT: genomic steiner alignments / Giuseppe Lancia and R. Ravi
• Bounds on the number of string subsequences / Daniel S. Hirschberg
• Approximate periods of strings / Jeong Seop Sim [and others]
• Finding maximal pairs with bounded gap / Gerth Stølting Brodal [and others]
• A dynamic data structure for reverse lexicographically sorted prefixes / Hidetoshi Yokoo
• A new indexing method for approximate string matching / Gonzalo Navarro and Ricardo Baeza-Yates
• The compression of subsegments of images described by finite automata / Juhani Karhumäki, Wojciech Plandowski, and Wojciech Rytter
• Ziv Lempel compression of huge natural language data tries using suffix arrays / Strahil Ristov and Eric Laporte
• Matching of spots in 2D electrophoresis images. Point matching under non-uniform distortions / Tatsuya Akutsu [and others]
• Applying an edit distance to the matching of tree ring sequences in dendrochronology / Carola Wenk
• Fast multi-dimensional approximate pattern matching / Gonzalo Navarro and Ricardo Baeza-Yates
• Finding common RNA secondary structures from RNA sequences / Zhuozhi Wang and Kaizhong Zhang
• Finding common subsequences with arcs and pseudoknots / Patricia A. Evans
• Computing similarity between RNA structures / Kaizhong Zhang, Lusheng Wang, and Bin Ma.

This book constitutes the refereed proceedings of the 10th Annual Symposium on Combinatorial Pattern Matching, CPM 99, held in Warwick, UK in July 1999. The 21 revised papers presented were carefully reviewed and selected from 26 submissions. The papers address all current issues in combinatorial pattern matching dealing with a variety of classical objects like trees, regular expressions, graphs, point sets, and arrays as well as with DNA/RNA coding, WWW issues, information retrieval, data compression, and pattern recognition.

• Front Cover; Dynamic Programming and the Calculus of Variations; Copyright Page; Preface; Contents;
• Chapter I. Discrete Dynamic Programming;
• Chapter II. The Classical Variational Theory;
• Chapter III. The Simplest Problem;
• Chapter IV. The Problem of Mayer;
• Chapter V. Inequality Constraints;
• Chapter VI. Problems with Special Linear Structures;
• Chapter VII. Stochastic and Adaptive Optimization Problems; Bibliography; Author Index; Subject Index.

Dynamic programming and the calculus of variations.

• Front Cover; Dynamic Programming and the Calculus of Variations; Copyright Page; Preface; Contents;
• Chapter I. Discrete Dynamic Programming;
• Chapter II. The Classical Variational Theory;
• Chapter III. The Simplest Problem;
• Chapter IV. The Problem of Mayer;
• Chapter V. Inequality Constraints;
• Chapter VI. Problems with Special Linear Structures;
• Chapter VII. Stochastic and Adaptive Optimization Problems; Bibliography; Author Index; Subject Index.

Dynamic programming and the calculus of variations.

• Invited Lectures.- Identifying and Filtering Near-Duplicate Documents.- Machine Learning for Efficient Natural-Language Processing.- Browsing around a Digital Library: Today and Tomorrow.- Summer School Lectures.- Algorithmic Aspects of Speech Recognition: A Synopsis.- Some Results on Flexible-Pattern Discovery.- Contributed Papers.- Explaining and Controlling Ambiguity in Dynamic Programming.- A Dynamic Edit Distance Table.- Parametric Multiple Sequence Alignment and Phylogeny Construction.- Tsukuba BB: A Branch and Bound Algorithm for Local Multiple Sequence Alignment.- A Polynomial Time Approximation Scheme for the Closest Substring Problem.- Approximation Algorithms for Hamming Clustering Problems.- Approximating the Maximum Isomorphic Agreement Subtree Is Hard.- A Faster and Unifying Algorithm for Comparing Trees.- Incomplete Directed Perfect Phylogeny.- The Longest Common Subsequence Problem for Arc-Annotated Sequences.- Boyer-Moore String Matching over Ziv-Lempel Compressed Text.- A Boyer-Moore Type Algorithm for Compressed Pattern Matching.- Approximate String Matching over Ziv-Lempel Compressed Text.- Improving Static Compression Schemes by Alphabet Extension.- Genome Rearrangement by Reversals and Insertions/Deletions of Contiguous Segments.- A Lower Bound for the Breakpoint Phylogeny Problem.- Structural Properties and Tractability Results for Linear Synteny.- Shift Error Detection in Standardized Exams.- An Upper Bound for Number of Contacts in the HP-Model on the Face-Centered-Cubic Lattice (FCC).- The Combinatorial Partitioning Method.- Compact Suffix Array.- Linear Bidirectional On-Line Construction of Affix Trees.- Using Suffix Trees for Gapped Motif Discovery.- Indexing Text with Approximate q-Grams.- Simple Optimal String Matching Algorithm.- Exact and Efficient Computation of the Expected Number of Missing and Common Words in Random Texts.- Periods and Quasiperiods Characterization.- Finding Maximal Quasiperiodicities in Strings.- On the Complexity of Determining the Period of a String.
• (source: Nielsen Book Data)

The papers contained in this volumewere presented at the 11thAnnual Sym- sium on CombinatorialPattern Matching, held June 21-23, 2000 at the Univ- sit edeMontr eal. They were selected from 44 abstracts submitted in response to the call for papers. In addition, there were invited lectures by Andrei Broder (AltaVista), Fernando Pereira (AT&T Research Labs), and Ian H. Witten (U- versity of Waikato). The symposium was preceded by a two-day summer school set up to - tract and train young researchers. The lecturers at the school were Greg Butler, ClementLam, andGusGrahne:BLAST!Howdoyousearchsequencedatabases?, DavidBryant:Phylogeny, Ra aeleGiancarlo: Algorithmicaspectsof speech rec- nition, Nadia El-Mabrouk: Genome rearrangement, LaxmiParida: Flexib- pattern discovery, and Ian H. Witten: Adaptive text mining: inferring structure from sequences. Combinatorial Pattern Matching (CPM) addresses issues of searching and matching strings and more complicated patterns such as trees, regular expr- sions graphs, point sets, and arrays. The goal is to derive non-trivial combi- torial properties of such structures and to exploit these properties in order to achieve superior performance for the corresponding computational problems. Over recent years a steady ?ow of high-quality research on this subject has changed a sparse set of isolated results into a fully-?edged area of algorithmics.
(source: Nielsen Book Data)

• Introduction.- Feature Selection.- Grammatical Evolution.- Grammar Based Feature Generation.- Application of Grammar Framework to Time-series Prediction.- Case Studies.- Conclusion.
• (source: Nielsen Book Data)

This book proposes a novel approach for time-series prediction using machine learning techniques with automatic feature generation. Application of machine learning techniques to predict time-series continues to attract considerable attention due to the difficulty of the prediction problems compounded by the non-linear and non-stationary nature of the real world time-series. The performance of machine learning techniques, among other things, depends on suitable engineering of features. This book proposes a systematic way for generating suitable features using context-free grammar. A number of feature selection criteria are investigated and a hybrid feature generation and selection algorithm using grammatical evolution is proposed. The book contains graphical illustrations to explain the feature generation process. The proposed approaches are demonstrated by predicting the closing price of major stock market indices, peak electricity load and net hourly foreign exchange client trade volume. The proposed method can be applied to a wide range of machine learning architectures and applications to represent complex feature dependencies explicitly when machine learning cannot achieve this by itself. Industrial applications can use the proposed technique to improve their predictions.
(source: Nielsen Book Data)

• Computing Similarity between RNA Strings / Vineet Bafna, S. Muthukrishnan and R. Ravi
• Of Chicken Teeth and Mouse Eyes, or Generalized Character Compatibility / Craig Benham, Sampath Kannan and Tandy Warnow
• Efficient String Matching on Coded Texts / Dany Breslauer and Leszek Gasieniec
• Fast Approximate Matching using Suffix Trees / Archie L. Cobbs
• Common Subsequences and Supersequences and Their Expected Length / Vlado Dancik
• Pattern Matching in Directed Graphs / Jianghai Fu
• Constant-Space String Matching with Smaller Number of Comparisons: Sequential Sampling / Leszek Gasieniec, Wojciech Plandowski and Wojciech Rytter
• Multi-Dimensional Pattern Matching with Dimensional Wildcards / Raffaele Giancarlo and Roberto Grossi
• Minimizing Phylogenetic Number to Find Good Evolutionary Trees / Leslie Ann Goldberg, Paul W. Goldberg, Cynthia A. Phillips, Elizabeth Sweedyk and Tandy Warnow.

This volume presents the proceedings of the 6th International Symposium on Combinatorial Pattern Matching, CPM '95, held in Espoo, Finland in July 1995. CPM addresses issues of searching and matching strings and more complicated patterns such as trees, regular expressions, extended expressions, etc. The aim is to derive non-trivial combinatorial properties in order to improve the performance of the corresponding computational problems. This volume presents 27 selected refereed full research papers and two invited papers; it addresses all current aspects of CPM and its applications such as the design and analysis of algorithms for pattern matching problems in strings, graphs, and hypertexts, as well as in biological sequences and molecules.

• Front Cover; Computer-Oriented Approaches to Pattern Recognition; Copyright Page; Contents; Preface;
• CHAPTER I. BASIC CONCEPTS AND METHODS IN MATHEMATICAL PATTERN RECOGNITION;
• CHAPTER II. THE STATISTICAL FORMULATION AND PARAMETRIC METHODS;
• CHAPTER III. INTRODUCTION TO OPTIMIZATION TECHNIQUES;
• CHAPTER IV. LINEAR DISCRIMINANT FUNCTIONS AND EXTENSIONS;
• CHAPTER V. INDIRECT APPROXIMATION OF PROBABILITY DENSITIES;
• CHAPTER VI. DIRECT CONSTRUCTION OF PROBABILITY DENSITIES : POTENTIAL FUNCTIONS (PARZEN ESTIMATORS);
• CHAPTER VII. PIECEWISE LINEAR DISCRIMINANT FUNCTIONS.

Computer-oriented approaches to pattern recognition.

• Front Cover; Computer-Oriented Approaches to Pattern Recognition; Copyright Page; Contents; Preface;
• CHAPTER I. BASIC CONCEPTS AND METHODS IN MATHEMATICAL PATTERN RECOGNITION;
• CHAPTER II. THE STATISTICAL FORMULATION AND PARAMETRIC METHODS;
• CHAPTER III. INTRODUCTION TO OPTIMIZATION TECHNIQUES;
• CHAPTER IV. LINEAR DISCRIMINANT FUNCTIONS AND EXTENSIONS;
• CHAPTER V. INDIRECT APPROXIMATION OF PROBABILITY DENSITIES;
• CHAPTER VI. DIRECT CONSTRUCTION OF PROBABILITY DENSITIES : POTENTIAL FUNCTIONS (PARZEN ESTIMATORS);
• CHAPTER VII. PIECEWISE LINEAR DISCRIMINANT FUNCTIONS.

Computer-oriented approaches to pattern recognition.

• Introduction
• Basics of Affine Geometry
• Basic Properties of Convex Sets
• Embedding an Affine Space in a Vector Space
• Basics of Projective Geometry
• Basics of Euclidean Geometry
• Separating and Supporting Hyperplanes; Polar Duality
• Polytopes and Polyhedra
• The Cartan-Dieudonnþe Theorem
• The Quaternions and the Spaces S3, SU(2), SO(3), and RP3
• Dirichlet-Voronoi Diagrams
• Basics of Hermitian Geometry
• Spectral Theorems
• Singular Value Decomposition (SVD) and Polar Form
• Applications of SVD and Pseudo-Inverses
• Quadratic Optimization Problems
• Schur Complements and Applications
• Quadratic Optimization and Contour Grouping
• Basics of Manifolds and Classical Lie Groups
• Basics of the Differential Geometry of Curves
• Basics of the Differential Geometry of Surfaces
• Appendix
• References
• Symbol Index
• IndexAppendix
• References
• Symbol Index
• Index.

This book is an introduction to the fundamental concepts and tools needed for solving problems of a geometric nature using a computer. It attempts to fill the gap between standard geometry books, which are primarily theoretical, and applied books on computer graphics, computer vision, robotics, or machine learning. This book covers the following topics: affine geometry, projective geometry, Euclidean geometry, convex sets, SVD and principal component analysis, manifolds and Lie groups, quadratic optimization, basics of differential geometry, and a glimpse of computational geometry (Voronoi diagrams and Delaunay triangulations). Some practical applications of the concepts presented in this book include computer vision, more specifically contour grouping, motion interpolation, and robot kinematics. In this extensively updated second edition, more material on convex sets, Farkas's lemma, quadratic optimization and the Schur complement have been added. The chapter on SVD has been greatly expanded and now includes a presentation of PCA. The book is well illustrated and has chapter summaries and a large number of exercises throughout. It will be of interest to a wide audience including computer scientists, mathematicians, and engineers. Reviews of first edition: "Gallier's book will be a useful source for anyone interested in applications of geometrical methods to solve problems that arise in various branches of engineering. It may help to develop the sophisticated concepts from the more advanced parts of geometry into useful tools for applications." (Mathematical Reviews, 2001) " ... it will be useful as a reference book for postgraduates wishing to find the connection between their current problem and the underlying geometry." (The Australian Mathematical Society, 2001).

• Overview and State-of-the-Art of Uncertainty Visualization
• Uncertainty Visualization and Color Vision Deficiency
• Analysis of Uncertain Scalar Data with Hixels
• On the (Un)Suitability of Strict Feature Definitions for Uncertain Data
• The Haunted Swamps of Heuristics: Uncertainty in Problem Solving
• Visualizing Uncertainty in Predictive Models
• Incorporating Uncertainty in Intrusion Detection to Enhance Decision Making
• Fuzzy Fibers: Uncertainty in dMRI Tractography
• Mathematical Foundations of Uncertain Field Visualization
• Definition of a Multifield
• Categorization
• Fusion of Visual Channels
• Glyph-Based Multifield Visualization
• Derived Fields
• Interactive Visual Exploration and Analysis
• Visual Exploration of Multivariate Volume Data Based on Clustering
• Feature-Based Visualization of Multifields
• Feature Analysis in Multifields
• Future Challenges and Unsolved Problems in Multi-Field Visualization
• Overview of Visualization in Biology and Medicine
• Visualization in Connectomics
• Visualization in Biology and Medicine
• From Individual to Population: Challenges in Medical Visualization
• The Ultrasound Visualization Pipeline
• Visual Exploration of Simulated and Measured Blood Flow
• Large-Scale Integration-Based Vector Field Visualization
• Large Scale Data Analysis
• Cross-Scale, Multi-Scale, and Multi-Source Data Visualization and Analysis Issues and Opportunities
• Scalable Devices
• Scalable Representation
• Distributed Post-Processing and Rendering for Large-Scale Scientific Simulations.

Based on the seminar that took place in Dagstuhl, Germany in June 2011, this contributed volume studies the four important topics within the scientific visualization field: uncertainty visualization, multifield visualization, biomedical visualization and scalable visualization.? Uncertainty visualization deals with uncertain data from simulations or sampled data, uncertainty due to the mathematical processes operating on the data, and uncertainty in the visual representation, ? Multifield visualization addresses the need to depict multiple data at individual locations and the combination of multiple datasets, ? Biomedical is a vast field with select subtopics addressed from scanning methodologies to structural applications to biological applications, ? Scalability in scientific visualization is critical as data grows and computational devices range from hand-held mobile devices to exascale computational platforms. Scientific Visualization will be useful to practitioners of scientific visualization, students interested in both overview and advanced topics, and those interested in knowing more about the visualization process.

• Background and Concepts.- A Correspondence-Based Neural Model for Face Recognition.- Switchyards-Routing Structures in the Brain.- Ontogenesis of Switchyards.- Putting the Pieces Together: Recognition with Switchyards.- Discussion and Outlook.
• (source: Nielsen Book Data)

At?rstsight, thisbookisaboutfacerecognitioninthebrain.Itsmorelasting value, however, lies in the paradigmatic way in which this particular problem is treated. From the basic ideas that are worked out here in concrete detail, it is a naturaland simple next step to at leastimagine, if not realizein model form, much more generalstructures and processes, thus helping to bridge the still tremendous chasm between mind and brain. It is the purpose of this foreword to point out these generic traits. For centuries, thinking about the brain has been dominated by the most complexmechanisticdevicesofthetime, clockwork, communicatinghydraulic tubesor, today, thecomputer.Thecomputer, takenasincarnationoftheU- versal Turing Machine, can implement any conceivable process, so that also a functional brain can surely be simulated on it, an idea that, beginning in the ?fties of the last century, has been seducing scientists to create "art- cial intelligence" in the computer. As a result we now have an information technology that displays many functional capabilities formerly regarded as the exclusive domain of the mind. As fascinating as this is, doting on "int- ligent machines" is systematically diverting our attention awayfrom the true problems of understanding the working of the brain.
(source: Nielsen Book Data)