By Richard O. Duda, Peter E. Hart, David G. Stork

**Publish yr note:** First released in 1973

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The first variation, released in 1973, has turn into a vintage reference within the box. Now with the second one version, readers will locate details on key new subject matters similar to neural networks and statistical development acceptance, the speculation of computing device studying, and the speculation of invariances. additionally integrated are labored examples, comparisons among diverse equipment, broad pictures, accelerated workouts and machine undertaking topics.

An Instructor's guide proposing unique ideas to the entire difficulties within the ebook is out there from the Wiley editorial division.

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**Extra resources for Pattern Classification (2nd Edition)**

**Sample text**

12 - \12)p(x\w2)P{w2), (17) and otherwise decide co2. 21 - A n P{o)\) . to) This form of the decision rule focuses on the x-dependence of the probability densities. , the likelihood function) and then form the likelihood ratio p{x\œi)/p{x\cL>2). Thus the Bayes decision rule can be interpreted as calling for deciding a>\ if the likelihood ratio exceeds a threshold value that is independent of the observation x. 3 MINIMUM-ERROR-RATE CLASSIFICATION ZERO-ONE LOSS In classification problems, each state of nature is usually associated with a different one of the c classes, and the action a, is usually interpreted as the decision that the true state of nature is &>,■.

The risk corresponding to this loss function is precisely the average probability of error because the conditional risk is c R(cti\x) = J2k(a,\a>j)P(eoj\x) y=i = £>K|x) = l-P(œi\x) (20) *We note that other loss functions, such as quadratic and linear difference, find greater use in regression tasks where there is a natural ordering on the predictions and we can meaningfully penalize predictions that are "more wrong" than others. 3. The likelihood ratio p(x\co^)/p(x\a)2) for the distributions shown in Fig.

3. The likelihood ratio p(x\co^)/p(x\a)2) for the distributions shown in Fig. 2 . 1 . If we employ a zero-one or classification loss, our decision boundaries are determined by the threshold 6a. If our loss function penalizes miscategorizing u>2 as a>i patterns more than the converse, we get the smaller threshold 0b, and hence TZi becomes smaller. and P(û)j\x) is the conditional probability that action a, is correct. The Bayes decision rule to minimize risk calls for selecting the action that minimizes the conditional risk.

### Pattern Classification (2nd Edition) by Richard O. Duda, Peter E. Hart, David G. Stork

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