Machine learning : an algorithmic perspective

著者

• Marsland, Stephen

書誌事項

Machine learning : an algorithmic perspective

Stephen Marsland

（Chapman & Hall/CRC machine learning & pattern recognition series）

Chapman & Hall/CRC, c2009

注記

Includes bibliographical references and index

内容説明・目次

内容説明

Traditional books on machine learning can be divided into two groups - those aimed at advanced undergraduates or early postgraduates with reasonable mathematical knowledge and those that are primers on how to code algorithms. The field is ready for a text that not only demonstrates how to use the algorithms that make up machine learning methods, but also provides the background needed to understand how and why these algorithms work. Machine Learning: An Algorithmic Perspective is that text. Theory Backed up by Practical Examples The book covers neural networks, graphical models, reinforcement learning, evolutionary algorithms, dimensionality reduction methods, and the important area of optimization. It treads the fine line between adequate academic rigor and overwhelming students with equations and mathematical concepts. The author addresses the topics in a practical way while providing complete information and references where other expositions can be found. He includes examples based on widely available datasets and practical and theoretical problems to test understanding and application of the material. The book describes algorithms with code examples backed up by a website that provides working implementations in Python. The author uses data from a variety of applications to demonstrate the methods and includes practical problems for students to solve. Highlights a Range of Disciplines and Applications Drawing from computer science, statistics, mathematics, and engineering, the multidisciplinary nature of machine learning is underscored by its applicability to areas ranging from finance to biology and medicine to physics and chemistry. Written in an easily accessible style, this book bridges the gaps between disciplines, providing the ideal blend of theory and practical, applicable knowledge.

目次

Introduction If Data Had Mass, The Earth Would Be a Black Hole Learning Types of Machine Learning Supervised Learning The Brain and the Neuron Linear Discriminants Preliminaries The Perceptron Linear Separability Linear Regression The Multi-Layer Perceptron Going Forwards Going Backwards: Back-propagation of Error The Multi-Layer Perceptron in Practice Examples of Using the MLP Overview Back-propagation Properly Radial Basis Functions and Splines Concepts The Radial Basis Function (RBF) Network The Curse of Dimensionality Interpolation and Basis Functions Support Vector Machines Optimal Separation Kernels Learning With Trees Using Decision Trees Constructing Decision Trees Classification And Regression Trees (CART) Classification Example Decision by Committee: Ensemble Learning Boosting Bagging Different Ways to Combine Classifiers Probability and Learning Turning Data into Probabilities Some Basic Statistics Gaussian Mixture Models Nearest Neighbour Methods Unsupervised Learning The k-Means Algorithm Vector Quantisation The Self-Organising Feature Map Dimensionality Reduction Linear Discriminant Analysis (LDA) Principal Components Analysis (PCA) Factor Analysis Independent Components Analysis (ICA) Locally Linear Embedding Isomap Optimisation and Search Going Downhill Least-Squares Optimisation Conjugate Gradients Search: Three Basic Approaches Exploitation and Exploration Simulated Annealing Evolutionary Learning The Genetic Algorithm (GA) Generating Offspring: Genetic Operators Using Genetic Algorithms Genetic Programming Combining Sampling with Evolutionary Learning Reinforcement Learning Overview Example: Getting Lost Markov Decision Processes Values Back On Holiday: Using Reinforcement Learning The Difference Between Sarsa and Q-Learning Uses of Reinforcement Learning Markov Chain Monte Carlo (MCMC) Methods Sampling Monte Carlo or Bust The Proposal Distribution Markov Chain Monte Carlo Graphical Models Bayesian Networks Markov Random Fields Hidden Markov Models (HMM) Tracking Methods Python Installing Python and Other Packages Getting Started Code Basics Using NumPy and Matplotlib

「Nielsen BookData」 より

詳細情報

• NII書誌ID(NCID): BA89985212
• ISBN:
  • 9781420067187
• LCCN: 2009007292
• 出版国コード: us
• タイトル言語コード: eng
• 本文言語コード: eng
• 出版地: Boca Raton
• ページ数/冊数: xvi, 390 p.
• 大きさ: 25 cm
• 分類:
  • LCC : Q325.5
  • DC22 : 006.3/1
• 件名:
  • LCSH : Machine learning
  • LCSH : Algorithms
• 親書誌ID:
  • BA89985201