Stat841f09 - Wiki Course Notes

# 1 does svm find a global minimum 34 naive bayes

This preview shows page 1. Sign up to view the full content.

This is the end of the preview. Sign up to access the rest of the document.

Unformatted text preview: 33.1 Does SVM find a global minimum? 34 Naive Bayes, Decision Trees, K Nearest Neighbours, Boosting, and Bagging - November 25, 2009 34.1 Naive Bayes Classifiers 34.2 Decision Trees 34.2.1 Common Node Impurity Measures 34.3 K- Nearest Neighbours Classification 34.3.1 Property[42] 34.4 Boosting 34.4.1 AdaBoost Algorithm 34.4.2 AnyBoost 34.5 Bagging 34.6 Example 34.6.1 Random Forests wikicour senote.com/w/index.php?title= Stat841&amp;pr intable= yes 3/74 10/09/2013 Stat841 - Wiki Cour se Notes Proposal Mark your contribution here (http://spreadsheets.google.com/ccc? key=0Avbf0U1TJOcfdFFQR3NIc1pYUEdWeFdwbnNTUlRYZ3c&amp;hl=en|) Scribe sign up Classfication-2009.9.30 Clas s ification With the rise of fields such as data- mining, bioinformatics, and machine learning, classification has becomes a fast- developing topic. In the age of information, vast amounts of data are generated constantly, and the goal of classification is to learn from data. Potential application areas include handwritten post codes recognition, medical diagnosis, face recognition, human language processing and so on. De finition: The problem of Prediction a discrete random variable from another random variable is called Classification. In classification,, we attempt to approximate a function , by using a training data set, which will then be able to accurately classify new data inputs. Given , a subset of the d- dimensional real vectors and , a finite set of labels, We try to determine a 'clas s ification rule ' We use ordered pairs of training data which are identical independent distributions, approximate . Thus, given a new input, such that, where by using the classification rule we can predict a corresponding , , to . Example Suppose we wish to classify fruits into apples and oranges by considering certain features of the fruit, for instance, color, diameter, and weight. Let and . The goal is to find a classification rule such that when a new fruit is presented based on its features, , our classification rule can classify it as either an apple or an orange, i.e., be...
View Full Document

Ask a homework question - tutors are online