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EE292D: Statistical Learning and Pattern Classification , Statistical learning and pattern classification covers the theory and heuristics of the most important and successful techniques in pattern classification and clustering, , Pattern Classification (2nd ed) by Richard O Duda, Peter E Hart and David G Stork (Wiley, , Chapter 2 Computer Exercise: 2
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Pattern Recognition - Computer Science & Engineering , Course Description This course will introduce the fundamentals of statistical pattern recognition First, we will focus on generative methods such as those based on Bayes decision theory and related techniques of parameter estimation and density estimation
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(PDF) Pattern Classification - researchgate To develop the pattern classification algorithm, a supervised learning approach was used where 2-categorical groups were labelled (supervised classes) according to the seed viability ( Duda et al .
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Chapter 3 Solutions | Pattern Classification 2nd Edition , (a) For scalar data fit to a Gaussian model, the maximum likelihood mean and variance are given by equations (16) and (17) Note that equation (16) is just the sample mean, while equation (17) is the (biased) sample variance
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