Machine Learning: An Algorithmic Perspective, Second Edition (Chapman & Hall/Crc Machine Learning & Pattern Recognition)

Machine Learning: An Algorithmic Perspective, Second Edition (Chapman & Hall/Crc Machine Learning & Pattern Recognition)

Stephen Marsland

Language: English

Pages: 457

ISBN: 1466583282

Format: PDF / Kindle (mobi) / ePub


A Proven, Hands-On Approach for Students without a Strong Statistical Foundation

Since the best-selling first edition was published, there have been several prominent developments in the field of machine learning, including the increasing work on the statistical interpretations of machine learning algorithms. Unfortunately, computer science students without a strong statistical background often find it hard to get started in this area.

Remedying this deficiency, Machine Learning: An Algorithmic Perspective, Second Edition helps students understand the algorithms of machine learning. It puts them on a path toward mastering the relevant mathematics and statistics as well as the necessary programming and experimentation.

New to the Second Edition

  • Two new chapters on deep belief networks and Gaussian processes
  • Reorganization of the chapters to make a more natural flow of content
  • Revision of the support vector machine material, including a simple implementation for experiments
  • New material on random forests, the perceptron convergence theorem, accuracy methods, and conjugate gradient optimization for the multi-layer perceptron
  • Additional discussions of the Kalman and particle filters
  • Improved code, including better use of naming conventions in Python

Suitable for both an introductory one-semester course and more advanced courses, the text strongly encourages students to practice with the code. Each chapter includes detailed examples along with further reading and problems. All of the code used to create the examples is available on the author’s website.

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be combined to give a single measure, the F1 measure, which can be written in terms of precision and recall as: F1 = 2 precision × recall precision + recall (2.7) 24 Machine Learning: An Algorithmic Perspective An example of an ROC curve. The diagonal line represents exactly chance, so anything above the line is better than chance, and the further from the line, the better. Of the two curves shown, the one that is further away from the diagonal line would represent a more accurate method.

values by using the pcnfwd function. However, you need to manually add the −1s on in this case, using: Neurons, Neural Networks, and Linear Discriminants FIGURE 3.6 55 The decision boundary computed by a Perceptron for the OR function. >>> # Add the inputs that match the bias node >>> inputs_bias = np.concatenate((inputs,-np.ones((np.shape(inputs)[0],1))), axis=1) >>> pcn.pcnfwd(inputs_bias,weights) The results on this test data are what you can use in order to compute the accuracy of the

input nodes 2. the inputs are fed forward through the network (Figure 4.6) • the inputs and the first-layer weights (here labelled as v) are used to decide whether the hidden nodes fire or not. The activation function g(·) is the sigmoid function given in Equation (4.2) above • the outputs of these neurons and the second-layer weights (labelled as w) are used to decide if the output neurons fire or not 3. the error is computed as the sum-of-squares difference between the network outputs and the

about how close it was to the boundary in the output, so we don’t know that this was a difficult example to classify. A more suitable output encoding is called 1-of-N encoding. A separate node is used to represent each possible class, and the target vectors consist of zeros everywhere except for in the one element that corresponds to the correct class, e.g., (0, 0, 0, 1, 0, 0) means that the correct result is the 4th class out of 6. We are therefore using binary output values (we want each output

assumed that the datatset is linearly separable. We know that this is not always the case, but if we have a non-linearly separable dataset, then we cannot satisfy the constraints for all of the datapoints. The solution is to introduce some slack variables ηi ≥ 0 so that the constraints become ti (wT xi + b) ≥ 1 − ηi . For inputs that are correct, we set ηi = 0. These slack variables are telling us that, when comparing classifiers, we should consider the case where one classifier makes a mistake

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