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It has been a long way since 1995, and many new techniques and important developments have taken place in the field of A.I. and more concretely, machine learning. Still, this book has aged very well, for two reasons: first, the fundamental techniques and concepts that every practitioner must understand and be able to make use of, like for example parametric techniques for density estimation (kNN), dimensionality reduction (PCA), mixture models, in addition to, of course, neural networks. Second, this book paves the way for moving on to modern techniques like deep energy models and deep belief networks with its last chapter on bayesian techniques.
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This book came out at about the same time as Ripley's, which has almost the same title, but in reverse. At the time, I liked Ripley's better, because it covered more things that were totally new to me. Then a friend said he had chosen Bishop for a course he was teaching, and I went back and reconsidered the two books. I soon found that my friend was right: Bishop's book is better laid out for a course in that it starts at the beginning (well, not quite the beginning--you do need to be fairly sophisticated mathematically) and works up, while Ripley's is more a collection of insights all at the same level; confusing to learn from. Bishop is able to cover both theoretical and practical aspects well. There certainly are topics that aren't covered, but the ones that are there fit together nicely, are accurate and up to date, and are easy to understand. It has migrated from my bookcase to my desk, where it now stays, and I reach for it often. To the reviewer who said "I was looking forward to a detailed insight into neural networks in this book. Instead, almost every page is plastered up with sigma notation", that's like saying about a book on music theory "Instead, almost every page is palstered with black-and-white ovals (some with sticks on the edge)." Or to the reviewer who complains this book is limited to the mathematical side of neural nets, that's like complaining about a cookbook on beef being limited to the carnivore side. If you want a non-technical overview, you can get that elsewhere, but if you want understanding of the techniques, you have to understand the math. Otherwise, there's no beef.
activation function algorithm approach approximation back-propagation basis function network Bayes Bayesian bias Chapter classification problems coefficients component computational consider corresponding covariance matrix data points data set decision boundary denotes density estimation density function derivatives determined dimensionality discriminant function discussed in Section eigenvalues eigenvectors equations evaluate example expression feed-forward Gaussian give given gradient descent Hessian matrix hidden units input data input space input variables input vector kernel linear discriminant maximum likelihood minimization minimum mixture model multi-layer perceptron network function network mapping network outputs neural networks non-linear normal obtain optimal output units output variables parameters polynomial posterior distribution posterior probabilities pre-processing prior probabilities probability density procedure quadratic radial basis function regression regularization represents respect result search direction shown in Figure sigmoid simple solution sum-of-squares error function target data target values techniques term theorem tion training data training set transformation variance weight space weight values weight vector
From Google Scholar
Christopher JC Burges - 1998 - Data Mining and Knowledge Discovery
Fred Glover, Rafael Marti
2000 - IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
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2002 - IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
citeulike: Neural Networks for Pattern Recognition
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JSTOR: Neural Networks for Pattern Recognition.
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15-496/782 Syllabus: Artificial Neural Networks