Probabilistic models for classification

Hongbo Deng, Yizhou Sun, Yi Chang, Jiawei Han

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

In machine learning, classification is considered an instance of the supervised learning methods, i.e., inferring a function from labeled training data. The training data consist of a set of training examples, where each example is a pair consisting of an input object (typically a vector) x = <x1,x2,…,xd> and a desired output value (typically a class label) y ∊ {C1,C2,…,CK}. Given such a set of training data, the task of a classification algorithm is to analyze the training data and produce an inferred function, which can be used to classify new (so far unseen) examples by assigning a correct class label to each of them. An example would be assigning a given email into “spam” or “non-spam” classes.

Original languageEnglish (US)
Title of host publicationData Classification
Subtitle of host publicationAlgorithms and Applications
PublisherCRC Press
Pages65-86
Number of pages22
ISBN (Electronic)9781466586758
ISBN (Print)9781466586741
DOIs
StatePublished - Jan 1 2014

ASJC Scopus subject areas

  • Economics, Econometrics and Finance(all)
  • Business, Management and Accounting(all)
  • Computer Science(all)

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