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 language | English (US) |
|---|---|
| Title of host publication | Data Classification |
| Subtitle of host publication | Algorithms and Applications |
| Publisher | CRC Press |
| Pages | 65-86 |
| Number of pages | 22 |
| ISBN (Electronic) | 9781466586758 |
| ISBN (Print) | 9781466586741 |
| DOIs | |
| State | Published - Jan 1 2014 |
Fingerprint
ASJC Scopus subject areas
- Economics, Econometrics and Finance(all)
- Business, Management and Accounting(all)
- Computer Science(all)
Cite this
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 proceeding › Chapter
}
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 -