Machine learning : an algorithmic perspective /: an algorithmic perspective. (2011)

• Record Type: Book
• Further Information: Note: Stephen Marsland.
• Other Names: Marsland, Stephen
• Contents: 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
• Publisher Details: Place of publication not identified : Chapman and Hall/CRC
• Publication Date: 2011
• Extent: 1 online resource, illustrations
• Subjects: 006.31; Machine learning; Algorithms
• Languages: English
• ISBNs: 9781420067194; 1420067192
• Access Rights: Legal Deposit; Only available on premises controlled by the deposit library and to one user at any one time; The Legal Deposit Libraries (Non-Print Works) Regulations (UK).
• Access Usage: Restricted: Printing from this resource is governed by The Legal Deposit Libraries (Non-Print Works) Regulations (UK) and UK copyright law currently in force.