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An overview of the theory and application of kernel classification methods. Linear classifiers in kernel spaces have emerged as a major topic within the field of machine learning. The kernel technique takes the linear classifier-a limited, but well-established and comprehensively studied model-and extends its applicability to a wide range of nonlinear pattern-recognition tasks such as natural language processing, machine vision, and biological sequence analysis. This book provides the first comprehensive overview of both the theory and algorithms of kernel classifiers, including the most recent developments. It begins by describing the major algorithmic advances: kernel perceptron learning, kernel Fisher discriminants, support vector machines, relevance vector machines, Gaussian processes, and Bayes point machines. Then follows a detailed introduction to learning theory, including VC and PAC-Bayesian theory, data-dependent structural risk minimization, and compression bounds. Throughout, the book emphasizes the interaction between theory and algorithms: how learning algorithms work and why. The book includes many examples, complete pseudo code of the algorithms presented, and an extensive source code library.
(source: Nielsen Book Data)

An overview of the theory and application of kernel classification methods. Linear classifiers in kernel spaces have emerged as a major topic within the field of machine learning. The kernel technique takes the linear classifier-a limited, but well-established and comprehensively studied model-and extends its applicability to a wide range of nonlinear pattern-recognition tasks such as natural language processing, machine vision, and biological sequence analysis. This book provides the first comprehensive overview of both the theory and algorithms of kernel classifiers, including the most recent developments. It begins by describing the major algorithmic advances: kernel perceptron learning, kernel Fisher discriminants, support vector machines, relevance vector machines, Gaussian processes, and Bayes point machines. Then follows a detailed introduction to learning theory, including VC and PAC-Bayesian theory, data-dependent structural risk minimization, and compression bounds. Throughout, the book emphasizes the interaction between theory and algorithms: how learning algorithms work and why. The book includes many examples, complete pseudo code of the algorithms presented, and an extensive source code library.
(source: Nielsen Book Data)

An overview of the theory and application of kernel classification methods. Linear classifiers in kernel spaces have emerged as a major topic within the field of machine learning. The kernel technique takes the linear classifier-a limited, but well-established and comprehensively studied model-and extends its applicability to a wide range of nonlinear pattern-recognition tasks such as natural language processing, machine vision, and biological sequence analysis. This book provides the first comprehensive overview of both the theory and algorithms of kernel classifiers, including the most recent developments. It begins by describing the major algorithmic advances: kernel perceptron learning, kernel Fisher discriminants, support vector machines, relevance vector machines, Gaussian processes, and Bayes point machines. Then follows a detailed introduction to learning theory, including VC and PAC-Bayesian theory, data-dependent structural risk minimization, and compression bounds. Throughout, the book emphasizes the interaction between theory and algorithms: how learning algorithms work and why. The book includes many examples, complete pseudo code of the algorithms presented, and an extensive source code library.
(source: Nielsen Book Data)

Linear classifiers in kernel spaces have emerged as a major topic within the field of machine learning. The kernel technique takes the linear classifier--a limited, but well-established and comprehensively studied model--and extends its applicability to a wide range of nonlinear pattern-recognition tasks such as natural language processing, machine vision, and biological sequence analysis. This book provides the first comprehensive overview of both the theory and algorithms of kernel classifiers, including the most recent developments. It begins by describing the major algorithmic advances: kernel perceptron learning, kernel Fisher discriminants, support vector machines, relevance vector machines, Gaussian processes, and Bayes point machines. Then follows a detailed introduction to learning theory, including VC and PAC-Bayesian theory, data-dependent structural risk minimization, and compression bounds. Throughout, the book emphasizes the interaction between theory and algorithms: how learning algorithms work and why. The book includes many examples, complete pseudo code of the algorithms presented, and an extensive source code library

An overview of the theory and application of kernel classification methods. Linear classifiers in kernel spaces have emerged as a major topic within the field of machine learning. The kernel technique takes the linear classifier-a limited, but well-established and comprehensively studied model-and extends its applicability to a wide range of nonlinear pattern-recognition tasks such as natural language processing, machine vision, and biological sequence analysis. This book provides the first comprehensive overview of both the theory and algorithms of kernel classifiers, including the most recent developments. It begins by describing the major algorithmic advances: kernel perceptron learning, kernel Fisher discriminants, support vector machines, relevance vector machines, Gaussian processes, and Bayes point machines. Then follows a detailed introduction to learning theory, including VC and PAC-Bayesian theory, data-dependent structural risk minimization, and compression bounds. Throughout, the book emphasizes the interaction between theory and algorithms: how learning algorithms work and why. The book includes many examples, complete pseudo code of the algorithms presented, and an extensive source code library.
(source: Nielsen Book Data)

• Cover
• Copyright
• Credits
• Foreword
• About the Author
• Acknowledgments
• About the Reviewers
• www.PacktPub.com
• Table of Contents
• Preface
• Chapter 1: Introduction to Machine Learning
• Objective
• Getting in touch
• Different areas where machine learning is being used
• Why use F#?
• Supervised machine learning
• Training and test dataset/corpus
• Some motivating real life examples of supervised learning
• Nearest Neighbour algorithm (a.k.a k-NN algorithm)
• Distance metrics
• Decision tree algorithms
• Unsupervised learning
• Machine learning frameworks
• Machine learning for fun and profit
• Recognizing handwritten digits
• your "Hello World" ML program
• How does this work?
• Summary
• Chapter 2: Linear Regression
• Objective
• Different types of linear regression algorithms
• APIs used
• Math.NET Numerics for F# 3.7.0
• Getting Math.NET
• Experimenting with Math.NET
• The basics of matrices and vectors (a short and sweet refresher)
• Creating a vector
• Creating a matrix
• Finding the transpose of a matrix
• Finding the inverse of a matrix
• Trace of a matrix
• QR decomposition of a matrix
• SVD of a matrix
• Linear regression method of least square
• Finding linear regression coefficients using F#
• Finding the linear regression coefficients using Math.NET
• Putting it together with Math.NET and FsPlot
• Multiple linear regression
• Multiple linear regression and variations using Math.NET
• Weighted linear regression
• Plotting the result of multiple linear regression
• Ridge regression
• Multivariate multiple linear regression
• Feature scaling
• Summary
• Chapter 3: Classification Techniques
• Objective
• Different classification algorithms you will learn
• Some interesting things you can do
• Binary classification using k-NN
• How does it work?.

• Finding cancerous cells using k-NN: a case study
• Understanding logistic regression
• The sigmoid function chart
• Binary classification using logistic regression (using Accord.NET)
• Multiclass classification using logistic regression
• How does it work?
• Multiclass classification using decision trees
• Obtaining and using WekaSharp
• How does it work?
• Predicting a traffic jam using a decision tree: a case study
• Challenge yourself!
• Summary
• Chapter 4: Information Retrieval
• Objective
• Different IR algorithms you will learn
• What interesting things can you do?
• Information retrieval using tf-idf
• Measures of similarity
• Generating a PDF from a histogram
• Minkowski family
• L1 family
• Intersection family
• Inner Product family
• Fidelity family or squared-chord family
• Squared L2 family
• Shannon's Entropy family
• Similarity of asymmetric binary attributes
• Some example usages of distance metrics
• Finding similar cookies using asymmetric binary similarity measures
• Grouping/clustering color images based on Canberra distance
• Summary
• Chapter 5: Collaborative Filtering
• Objective
• Different classification algorithms you will learn
• Vocabulary of collaborative filtering
• Baseline predictors
• Basis of User-User collaborative filtering
• Implementing basic user-user collaborative filtering using F#
• Code walkthrough
• Variations of gap calculations and similarity measures
• Item-item collaborative filtering
• Top-N recommendations
• Evaluating recommendations
• Prediction accuracy
• Confusion matrix (decision support)
• Ranking accuracy metrics
• Prediction-rating correlation
• Working with real movie review data (Movie Lens)
• Summary
• Chapter 6: Sentiment Analysis
• Objective
• What you will learn
• A baseline algorithm for SA using SentiWordNet lexicons.

• Handling negations
• Identifying praise or criticism with sentiment orientation
• Pointwise Mutual Information
• Using SO-PMI to find sentiment analysis
• Summary
• Chapter 7: Anomaly Detection
• Objective
• Different classification algorithms
• Some cool things you will do
• The different types of anomalies
• Detecting point anomalies using IQR (Interquartile Range)
• Detecting point anomalies using Grubb's test
• Grubb's test for multivariate data using Mahalanobis distance
• Code walkthrough
• Chi-squared statistic to determine anomalies
• Detecting anomalies using density estimation
• Strategy to convert a collective anomaly to a point anomaly problem
• Dealing with categorical data in collective anomalies
• Summary
• Index.

Get up and running with machine learning with F# in a fun and functional way About This Book * Design algorithms in F# to tackle complex computing problems * Be a proficient F# data scientist using this simple-to-follow guide * Solve real-world, data-related problems with robust statistical models, built for a range of datasets Who This Book Is For If you are a C# or an F# developer who now wants to explore the area of machine learning, then this book is for you. Familiarity with theoretical concepts and notation of mathematics and statistics would be an added advantage. What You Will Learn * Use F# to find patterns through raw data * Build a set of classification systems using Accord.NET, Weka, and F# * Run machine learning jobs on the Cloud with MBrace * Perform mathematical operations on matrices and vectors using Math.NET * Use a recommender system for your own problem domain * Identify tourist spots across the globe using inputs from the user with decision tree algorithms In Detail The F# functional programming language enables developers to write simple code to solve complex problems. With F#, developers create consistent and predictable programs that are easier to test and reuse, simpler to parallelize, and are less prone to bugs. If you want to learn how to use F# to build machine learning systems, then this is the book you want. Starting with an introduction to the several categories on machine learning, you will quickly learn to implement time-tested, supervised learning algorithms. You will gradually move on to solving problems on predicting housing pricing using Regression Analysis. You will then learn to use Accord.NET to implement SVM techniques and clustering. You will also learn to build a recommender system for your e-commerce site from scratch. Finally, you will dive into advanced topics such as implementing neural network algorithms while performing sentiment analysis on your data. Style and approach This book is a fast-paced tutorial guide that uses hands-on examples to explain real-world applications of machine learning. Using practical examples, the book will explore several machine learning techniques and also describe how you can use F# to build machine learning systems.
(source: Nielsen Book Data)

• A guide to the NeurIPS 2018 competitions / Ralf Herbrich, Sergio Escalera
• Pommerman & NeurIPS 2018 / Cinjon Resnick, Chao Gao, Görög Márton, Takayuki Osogami, Liang Pang, Toshihiro Takahashi
• The AI Driving Olympics at NeurIPS 2018 / Julian Zilly, Jacopo Tani, Breandan Considine, Bhairav Mehta, Andrea F. Daniele, Manfred Diaz et al.
• Artificial intelligence for prosthetics : challenge solutions / Łukasz Kidziński, Carmichael Ong, Sharada Prasanna Mohanty, Jennifer Hicks, Sean Carroll, Bo Zhou et al.
• Adversarial vision challenge / Wieland Brendel, Jonas Rauber, Alexey Kurakin, Nicolas Papernot, Behar Veliqi, Sharada P. Mohanty et al.
• The inclusive images competition / James Atwood, Yoni Halpern, Pallavi Baljekar, Eric Breck, D. Sculley, Pavel Ostyakov et al.
• The second conversational intelligence challenge (ConvAI2) / Emily Dinan, Varvara Logacheva, Valentin Malykh, Alexander Miller, Kurt Shuster, Jack Urbanek et al.
• AutoML @ NeurIPS 2018 challenge : design and reuslts / Hugo Jair Escalante, Wei-Wei Tu, Isabelle Guyon, Daniel L. Silver, Evelyne Viegas, Yuqiang Chen et al.
• The tracking machine learning challenge : accuracy phase / Sabrina Amrouche, Laurent Basara, Paolo Calafiura, Victor Estrade, Steven Farrell, Diogo R. Ferreira et al.
• Efficient and robust learning on elaborated gaits with curriculum learning / Bo Zhou, Hongsheng Zeng, Fan Wang, Rongzhong Lian, Hao Tian

• ConvAI2 dataset of non-goal-oriented human-to-bot dialogues / Varvara Logacheva, Valentin Malykh, Aleksey Litinsky, Mikhail Burtsev
• Lost in conversation : a conversational agent based on the transformer and transfer learning / Sergey Golovanov, Alexander Tselousov, Rauf Kurbanov, Sergey I. Nikolenko
• Automatically optimized gradient boosting trees for classifying large volume high cardinality data streams under concept drift / Jobin Wilson, Amit Kumar Meher, Bivin Vinodkumar Bindu, Santanu Chaudhury, Brejesh Lall, Manoj Sharma et al.

This volume presents the results of the Neural Information Processing Systems Competition track at the 2018 NeurIPS conference. The competition follows the same format as the 2017 competition track for NIPS. Out of 21 submitted proposals, eight competition proposals were selected, spanning the area of Robotics, Health, Computer Vision, Natural Language Processing, Systems and Physics. Competitions have become an integral part of advancing state-of-the-art in artificial intelligence (AI). They exhibit one important difference to benchmarks: Competitions test a system end-to-end rather than evaluating only a single component; they assess the practicability of an algorithmic solution in addition to assessing feasibility. The eight run competitions aim at advancing the state of the art in deep reinforcement learning, adversarial learning, and auto machine learning, among others, including new applications for intelligent agents in gaming and conversational settings, energy physics, and prosthetics. .
(source: Nielsen Book Data)

• Vorwort
• Grusswort des Bundesministers / Peter Altmaier
• Vor der Tagung
• Künstliche Intelligenz : Thema und Kontext
• Die Träume der Vergangenheit werden Zukunft / Klaus Siebenhaar
• Künstliche Intelligenz bei Amazon : Forschung im Dienst des Kunden / Ralf Herbrich
• Prolog : Status, Perspektiven und Rahmenbedingungen
• Maschinelles Lernen : Entwicklung ohne Grenzen? / Bernhard Schölkopf
• Künstliche Intelligenz im Interesse der Gesellschaft gestalten : Ansätze für eine innovationsfreundliche Governance-Strategie / Wolfgang Schulz
• Forum I : Mensch, Maschine : Innovation und gesellschaftliche Verantwortung in Zeiten von Big Data und Künstlicher Intelligenz
• Der Mensch muss ein neues Selbstverständnis finden / Joachim M. Buhmann
• Der Zusammenhalt in der Gesellschaft muss gewahrt bleiben / Jens Zimmermann
• Podiumsdiskussion / Mit Andreas Boes, Joachim M. Buhmann, Lea Helmers; moderiert von Joana Breidenbach
• Kl : Die Tagung in Bildern
• Fortschritt und Vertrauen : Künstliche Intelligenz in Alltag, Lebenswelt und Öffentlichkeit
• Die algorithmische Gesellschaft : Wie wir Künstliche Intelligenz in unsere Dienste stellen / Jörg Dräger
• Wir wollen den Fortschritt gern gestalten / Gottfried Ludewig
• Podiumsdiskussion / Mit Christian Bauckhage, Jörg Dräger, Martin Hirsch, Dirk Kretzschmar, Gottfried Ludewig; moderiert von Joachim Bühler
• "Schlaue Regulierung : Rahmenbedingungen für Kl in Deutschland und Europa
• Für ein europäisches Kl-Leitbild / Andreas Boes, Elisabeth Vogl
• Podiumsdiskussion / Mit Aljoscha Burchardt, Lorena Jaume-Palasí, Martina Mara; moderiert von Wolfgang Schulz
• Epilog
• Olimpias Erbe : Kunst, künstliche Intelligenz und Kreativität / Klaus Siebenhaar
• Kl : Die Tagung in Bildern
• Anhang
• Zu den Autoren, Herausgebern und Gesprächspartnern
• Veranstalter und Partner
• Bildnachweis, Impressum