Pattern classification
by Richard O. Duda, Peter E. Hart, David G. Stork · first published 2001
89% recommend11% neutral
Why HN recommends it
Practical introduction to pattern recognition and machine learning covering many classic classification algorithms; well written, assumes little background, and suits students and engineers starting in the field.
Summarised by Claude from recommending comments
"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
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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…
on “A Tour of Machine Learning Algorithms” ↗A great textbook as an intro is by Duda and Hart, Pattern Classification. http://www.amazon.com/Pattern-Classification-2nd-Richard-Dud.... Its pretty well written and gives a good overview of the main techniques. If you want a bit more theory, try Cherkassky and Muller, "Learning…
on “Ask HN: How do I start with Machine Learning?” ↗Of the top of my head, - Machine Learning by Tom M Mitchell http://www.cs.cmu.edu/~tom/mlbook.html For general reading and introductions I also like: - Pattern Classification by Richard Duda - Pattern Recognition and Machine Learning by Christopher Bishop For a bit more emphasis on statistics and math, I usually dive in to - Classification,Parameter Estimation and State…
on “Think Stats: Probability and Statistics for Programmers” ↗…fact that it's available for free is just an added bonus. I would also suggest Elements of Statistical Learning: http://www-stat.stanford.edu/~tibs/ElemStatLearn/ As well as Duda, Hart, and Stork's Pattern Classification: http://rii.ricoh.com/~stork/DHS.html
on “Can anyone recommend good books, articles or essays that introduce machine learning?” ↗…Algebra, Numerical Computation/Optimization (Convex, Nonlinear), Statistical Inference, Stochastic Processes. Good References: 1) Elements of Statistical Learning - Hastie, Tibshirani and Friedman 2) Pattern Classification - Duda, Hart and Stork 3) Pattern Recognition - Theoridis, Koutroumbas 4) Machine Learning - Tom Mitchell 5) http://videolectures.net/Top/Computer_Science/Machine_Learni...
on “Advice for AI aspirations?” ↗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…
on “Elements of Statistical Learning” ↗…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"…
on “USA tops International Math Olympiad for first time in 21 years” ↗…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…
on “Ask HN: What maths are critical to pursuing ML/AI?” ↗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…
on “Ask HN: Best way to get started with AI?” ↗…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.
on “Monte Carlo Long-Range Interacting System Simulations” ↗…project or employer, so it’s a relative waste of time unless it is the only way you can get a job. [0]: < https://www.amazon.com/Deep-Learning-Adaptive-Computation-Ma... > [1]: < https://www.amazon.com/Pattern-Classification-Pt-1-Richard-D... > [2]: < https://www.amazon.com/Pattern-Recognition-Learning-Informat... > [3]: < http://www.web.stanford.edu/~hastie/ElemStatLearn/ > [4]: < http://www.stat.columbia.edu/~gelman/book/…
on “The Matrix Calculus You Need for Deep Learning” ↗