1  9
1. Semisupervised learning [2006]
 Cambridge, Mass. : MIT Press, c2006.
 Description
 Book — x, 508 p. : ill. ; 26 cm.
 Summary

A comprehensive review of an area of machine learning that deals with the use of unlabeled data in classification problems: stateoftheart algorithms, a taxonomy of the field, applications, benchmark experiments, and directions for future research. In the field of machine learning, semisupervised learning (SSL) occupies the middle ground, between supervised learning (in which all training examples are labeled) and unsupervised learning (in which no label data are given). Interest in SSL has increased in recent years, particularly because of application domains in which unlabeled data are plentiful, such as images, text, and bioinformatics. This first comprehensive overview of SSL presents stateoftheart algorithms, a taxonomy of the field, selected applications, benchmark experiments, and perspectives on ongoing and future research.SemiSupervised Learning first presents the key assumptions and ideas underlying the field: smoothness, cluster or lowdensity separation, manifold structure, and transduction. The core of the book is the presentation of SSL methods, organized according to algorithmic strategies. After an examination of generative models, the book describes algorithms that implement the lowdensity separation assumption, graphbased methods, and algorithms that perform twostep learning. The book then discusses SSL applications and offers guidelines for SSL practitioners by analyzing the results of extensive benchmark experiments. Finally, the book looks at interesting directions for SSL research. The book closes with a discussion of the relationship between semisupervised learning and transduction.
(source: Nielsen Book Data)
Engineering Library (Terman)
Engineering Library (Terman)  Status 

Stacks  
Q325.75 .S42 2006  Unknown 
 New York : Nova Science Publishers, [2018]
 Description
 Book — 1 online resource (241 pages)
 Summary

 Intro; SEMISUPERVISED LEARNINGBACKGROUND, APPLICATIONSAND FUTURE DIRECTIONS; SEMISUPERVISED LEARNINGBACKGROUND, APPLICATIONSAND FUTURE DIRECTIONS; CONTENTS; PREFACE; Introduction to This Book; Target Audience; Acknowledgments; Chapter 1CONSTRAINED DATASELFREPRESENTATIVE GRAPHCONSTRUCTION; Abstract;
 1. Introduction;
 2. Constrained Data SelfRepresentative GraphConstruction;
 3. Kernelized Variants; 3
 .1. Hilbert Space; 3
 .2. Column Generation;
 4. Performance Evaluation; 4
 .1. Label Propagation; 4.1
 .1. Gaussian Random Fields; 4.1
 .2. Local and Global Consistency; 4
 .2. Experimental Results
 4.2
 .1. Comparison among Several Graph Construction Methods4.2
 .2. Stability of the Proposed Method; 4.2
 .3. Sensitivity to Parameters; 4.2
 .4. Computational Complexity and CPU Time; Acknowledgments; Conclusion; References; Chapter 2INJECTING RANDOMNESS INTO GRAPHS:AN ENSEMBLE SEMISUPERVISEDLEARNING FRAMEWORK; Abstract;
 1. Introduction;
 2. Background; 2
 .1. GraphBased SemiSupervised Learning; 2
 .2. Ensemble Learning and Random Forests; 2
 .3. Anchor Graph;
 3. Random MultiGraphs; 3
 .1. Problem Formulation; 3
 .2. Algorithm; 3
 .3. Graph Construction; 3
 .4. SemiSupervised Inference
 3
 .5. Inductive Extension3
 .6. Randomness as Regularization;
 4. Experiments; 4
 .1. Data Sets; 4
 .2. Experimental Results; 4
 .3. Impact of Parameters; 4
 .4. Hyperspectral Image Classification; Acknowledgments; Conclusion; References; Chapter 3LABEL PROPAGATION VIA KERNELFLEXIBLE MANIFOLD EMBEDDING; Abstract;
 1. Introduction;
 2. RelatedWork; 2
 .1. SemiSupervised Discriminant Analysis; 2
 .2. SemiSupervised Discriminant Embedding; 2
 .3. Laplacian Regularized Least Square; 2
 .4. Review of the Flexible Manifold Embedding Framework;
 3. Kernel FlexibleManifold Embedding; 3
 .1. The Objective Function
 3
 .2. Optimal Solution3
 .3. The Algorithm; 3
 .4. Difference between KFME and Existing Methods; 3.4
 .1. Difference between KFME and FME; 3.4
 .2. Difference between KFME and Other Methods;
 4. Experimental Results; 4
 .1. Datasets; 4
 .2. Method Comparison; 4
 .3. Results Analysis; 4
 .4. Stability with Respect to Graph; Acknowledgments; Conclusion; References; Chapter 4FAST GRAPHBASED SEMISUPERVISEDLEARNING AND ITS APPLICATIONS; Abstract;
 1. Introduction;
 2. Related Work; 2
 .1. Scalable GraphBased SSL/TL Methods; 2
 .2. Scalable Graph Construction Methods; 2
 .3. Robust GraphBased SSL/TL Methods
 3. Minimum Tree Cut Method3
 .1. Notations; 3
 .2. The Proposed Method; 3
 .3. The Tree Labeling Algorithm; 3
 .4. Generate a Spanning Tree from a Graph;
 4. Insensitiveness to Graph Construction;
 5. Experiments; 5
 .1. Data Set; 5.1
 .1. UCI Data Set; 5.1
 .2. Image; 5.1
 .3. Text; 5
 .2. Graph Construction; 5
 .3. Accuracy; 5
 .4. Speed; 5
 .5. Robustness; 5
 .6. Effect of Different Spanning Tree and Ensemble of MultipleSpanning Trees;
 6. Applications in Text Extraction; 6
 .1. Interactive Text Extraction in Natural Scene Images; 6
 .2. Document Image Binarization; Conclusion and FutureWork; References
3. Graphbased semisupervised learning [2014]
 Subramanya, Amarnag, author.
 Cham, Switzerland : Springer, [2014]
 Description
 Book — 1 online resource (xiii, 111 pages) : illustrations
 Summary

 Introduction Graph Construction Learning and Inference Scalability Applications Future Work Bibliography Authors' Biographies Index.
 (source: Nielsen Book Data)
(source: Nielsen Book Data)
4. Introduction to semisupervised learning [2009]
 Zhu, Xiaojin, Ph. D.
 Cham, Switzerland : Springer, ©2009.
 Description
 Book — 1 online resource (xi, 116 pages) : color illustrations
 Summary

 Introduction to Statistical Machine Learning Overview of SemiSupervised Learning Mixture Models and EM CoTraining GraphBased SemiSupervised Learning SemiSupervised Support Vector Machines Human SemiSupervised Learning Theory and Outlook.
 (source: Nielsen Book Data)
(source: Nielsen Book Data)
5. Grokking deep Qnetworks [2020]
 [First edition].  [Place of publication not identified] : Manning Publications, 2020.
 Description
 Video — 1 online resource (1 video file (1 hr., 7 min.)) : sound, color. Sound: digital. Digital: video file.
 Summary

Miguel Morales, the master of RL domain and the author of "Grokking Deep Reinforcement Learning", demonstrates how to make reinforcement learning more like supervised learning with the help of the popular algorithm Deep QNetwork, which is still one of the best performing DRL agents.
 Abarbanel, H. D. I., author.
 Cambridge, United Kingdom ; New York, NY : Cambridge University Press, 2022.
 Description
 Book — 1 online resource
 Summary

 1. Prologue: linking 'The Future' with the present
 2. A data assimilation reminder
 3. Remembrance of things path
 4. SDA variational principles
 EulerLagrange equations and Hamiltonian formulation
 5. Using waveform information
 6. Annealing in the model precision Rf
 7. Discrete time integration in data assimilation variational principles
 Lagrangian and Hamiltonian formulations
 8. Monte Carlo methods
 9. Machine learning and its equivalence to statistical data assimilation
 10. Two examples of the practical use of data assimilation
 11. Unfinished business
 Bibliography
 Index.
 (source: Nielsen Book Data)
(source: Nielsen Book Data)
 Søgaard, Anders, 1981 author. Author
 Cham, Switzerland : Springer, ©2013.
 Description
 Book — 1 online resource (x, 93 pages) : illustrations
 Summary

 Introduction Supervised and Unsupervised Prediction SemiSupervised Learning Learning under Bias Learning under Unknown Bias Evaluating under Bias.
 (source: Nielsen Book Data)
(source: Nielsen Book Data)
8. Active learning [2012]
 Settles, Burr.
 Cham, Switzerland : Springer, ©2012.
 Description
 Book — 1 online resource (xiii, 100 pages) : illustrations
 Summary

 Automating Inquiry Uncertainty Sampling Searching Through the Hypothesis Space Minimizing Expected Error and Variance Exploiting Structure in Data Theory Practical Considerations.
 (source: Nielsen Book Data)
(source: Nielsen Book Data)
 Hastie, Trevor author.
 Second edition.  New York : Springer, [2009]
 Description
 Book — xxii, 745 pages : illustrations (some color), charts ; 24 cm.
 Summary

 1. Introduction
 2. Overview of supervised learning
 3. Linear methods for regression
 4. Linear methods for classification
 5. Basis expansions and regularization
 6. Kernel smoothing methods
 7. Model assessment and selection
 8. Model inference and averaging
 9. Additive models, trees, and related methods
 10. Boosting and additive trees
 11. Neural networks
 12. Support vector machines and flexible discriminants
 13. Prototype methods and nearestneighbors
 14. Unsupervised learning
 15. Random forests
 16. Ensemble learning
 17. Undirected graphical models
 18. Highdimensional problems: p>> N.
(source: Nielsen Book Data)
Business Library
Business Library  Status 

Stacks  Request (opens in new tab) 
Q325.75 .H37 2009  Unknown 
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