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

Fingerprint

Labels
Supervised learning
Electronic mail
Learning systems
Statistical Models
Probabilistic model

ASJC Scopus subject areas

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

Cite this

Deng, H., Sun, Y., Chang, Y., & Han, J. (2014). Probabilistic models for classification. In Data Classification: Algorithms and Applications (pp. 65-86). CRC Press. https://doi.org/10.1201/b17320

Probabilistic models for classification. / Deng, Hongbo; Sun, Yizhou; Chang, Yi; Han, Jiawei.

Data Classification: Algorithms and Applications. CRC Press, 2014. p. 65-86.

Research output: Chapter in Book/Report/Conference proceedingChapter

Deng, H, Sun, Y, Chang, Y & Han, J 2014, Probabilistic models for classification. in Data Classification: Algorithms and Applications. CRC Press, pp. 65-86. https://doi.org/10.1201/b17320
Deng H, Sun Y, Chang Y, Han J. Probabilistic models for classification. In Data Classification: Algorithms and Applications. CRC Press. 2014. p. 65-86 https://doi.org/10.1201/b17320
Deng, Hongbo ; Sun, Yizhou ; Chang, Yi ; Han, Jiawei. / Probabilistic models for classification. Data Classification: Algorithms and Applications. CRC Press, 2014. pp. 65-86
@inbook{83399f5609184830be593afa18175fc0,
title = "Probabilistic models for classification",
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 = 1,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.",
author = "Hongbo Deng and Yizhou Sun and Yi Chang and Jiawei Han",
year = "2014",
month = "1",
day = "1",
doi = "10.1201/b17320",
language = "English (US)",
isbn = "9781466586741",
pages = "65--86",
booktitle = "Data Classification",
publisher = "CRC Press",

}

TY - CHAP

T1 - Probabilistic models for classification

AU - Deng, Hongbo

AU - Sun, Yizhou

AU - Chang, Yi

AU - Han, Jiawei

PY - 2014/1/1

Y1 - 2014/1/1

N2 - 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 = 1,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.

AB - 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 = 1,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.

UR - http://www.scopus.com/inward/record.url?scp=84987666533&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84987666533&partnerID=8YFLogxK

U2 - 10.1201/b17320

DO - 10.1201/b17320

M3 - Chapter

AN - SCOPUS:84987666533

SN - 9781466586741

SP - 65

EP - 86

BT - Data Classification

PB - CRC Press

ER -