Name: Pattern classification
ISBN: 9780471056690

Pattern classification

"Practitioners developing or investigating pattern recognition systems in such diverse application areas as speech recognition, optical character recognition, image processing, or signal analysis, often face the difficult task of having to decide among a bewildering array of available techniques. This unique text/professional reference provides the information you need to choose the most appropriate method for a given class of problems, presenting an in-depth, systematic account of the major topics in pattern recognition today. A new edition of a classic work that helped define the field for over a quarter century, this practical book updates and expands the original work, focusing on pattern classification and the immense progress it has experienced in recent years."--BOOK JACKET.

What Hacker News says

wrpJul 2023

The relation between ANNs and statistics is a topic over which much ink was spilled in the 1990s. This is the most concise discussion I can think of:

Sarle, Warren S. (1994). Neural Networks and Statistical Models. Proceedings of the Nineteenth Annual SAS Users Group International Conference, April, 1994.

If you want an in-depth discussion, these books relate ANNs and statistical methods:

Schürmann, Jürgen. (1996). Pattern Classification: A Unified View of Statistical and Neural Approaches.

Raudys, Sarunas. (2001). Statistical and Neural Classifiers: An Integrated Approach to Design.

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chestervonwinchNov 2017

I know you say you'd like to learn online, but I highly recommend picking up Duda and Hart's Pattern Classification to have a theoretical complement to the "hands on", programming type introductions. It's a very accessible intro to the topic, but also covers a lot of material in depth -- in particular, the topics you mention.

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jochenleidnerAug 2017

1. You can get a long way with high school calculus and probability theory.

2. Regarding books I second the late David McKay's "Information Theory, Inference and Learning Algorithms" and the second edition of "Elements of Statistical Learning" by Tibshirani et al. (there's also a more accessible version of a subset of the material targeting MBA students called James et al., An Introduction to Statistical Learning). Duda/Hart/Stork's Pattern Classification (2nd ed.) is also great. The self-published volume by Abu-Mostafa/Magdon-Ismail/Lin, Learning from Data: A Short Course is impressive, short and useful for self-study.

3. Wikipedia is surprisingly good at providing help, and so is Stack Exchange, which has a statistics sub-forum, and of course there are many online MOOC courses on statistics/probability and more specialized ones on machine learning.

4. After that you will want to consult conference papers and online tutorials on particular models (k-means, Ward/HAC, HMM, SVM, perceptron, MLP, linear and logistic regression, kNN, multinomial naive Bayes, ...).

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chestervonwinchJul 2015

Casella & Berger's "Statistical Inference" is a nice introduction to basic probability theory and statistics. I found it pretty readable, and it's used for many 1st year graduate stat programs.

Duda & Hart's "Pattern Classification" is one of the best introductions to machine learning IMO. It assumes very little in the way prerequisites, which is nice for first time exposure.

Hastie & Tibshirani's "Elements of Statistical Learning" can be a little intimidating without having been exposed to the ideas of the previous two texts. Afterwards, however, it is a gem.

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mauritsMay 2014

The most popular choices seem to be:

Machine Learning: a Probabilistic Perspective, by Murphy

http://www.cs.ubc.ca/~murphyk/MLbook/

Pattern classification, by Duda et all

http://www.amazon.com/Pattern-Classification-Pt-1-Richard-Du...

The Elements of Statistical Learning, by Hastie et all. It is free from Stanford.

http://www-stat.stanford.edu/~tibs/ElemStatLearn

Mining of Massive Datasets, free from Stanford.

http://infolab.stanford.edu/~ullman/mmds.html

Bayesian Reasoning and Machine Learning, by Barber, free available online.

http://web4.cs.ucl.ac.uk/staff/D.Barber/pmwiki/pmwiki.php?n=...

Learning from data, by Abu-Mostafa.

It comes with Caltech video lectures: http://work.caltech.edu/telecourse.html

Pattern Recognition and Machine Learning, by Bischop

http://research.microsoft.com/en-us/um/people/cmbishop/prml/

Also noteworthy

Information Theory, Inference, and Learning Algorithms, by Mackay, free.

http://www.inference.phy.cam.ac.uk/itprnn/book.html

Classification, Parameter Estimation and State Estimation, by van der Heijden.

http://prtools.org

Computer Vision: Models, Learning, and Inference, by Prince, available for free

http://www.computervisionmodels.com/

Probabilistic Graphical Models, by Koller. Has an accompanying course on Coursera.

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atrillaMar 2013

Yep, Pattern Classification by Duda, Hart and Stork:

http://www.amazon.com/Pattern-Classification-2nd-Richard-Dud...

It is very pragmatic, including algorithms for many machine learning and artificial intelligence topics (from fitting functions for classification or regression purposes to search processes). The authors have a strong industrial background (in addition to the academic).

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