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123
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ICML
2006
IEEE
16 years 4 months ago
A new approach to data driven clustering
We consider the problem of clustering in its most basic form where only a local metric on the data space is given. No parametric statistical model is assumed, and the number of cl...
Arik Azran, Zoubin Ghahramani
131
Voted
IRAL
2003
ACM
15 years 9 months ago
Korean named entity recognition using HMM and CoTraining model
Namedentityrecognition isimportantinsophisticatedinformation service system such as Question Answering and Text Mining since most of the answer type and text mining unit depend on...
Euisok Chung, Yi-Gyu Hwang, Myung-Gil Jang
ICML
2003
IEEE
16 years 4 months ago
Identifying Predictive Structures in Relational Data Using Multiple Instance Learning
This paper introduces an approach for identifying predictive structures in relational data using the multiple-instance framework. By a predictive structure, we mean a structure th...
Amy McGovern, David Jensen
130
Voted
ICML
2009
IEEE
16 years 4 months ago
Structure learning with independent non-identically distributed data
There are well known algorithms for learning the structure of directed and undirected graphical models from data, but nearly all assume that the data consists of a single i.i.d. s...
Robert E. Tillman
139
Voted
COLT
2008
Springer
15 years 5 months ago
Does Unlabeled Data Provably Help? Worst-case Analysis of the Sample Complexity of Semi-Supervised Learning
We study the potential benefits to classification prediction that arise from having access to unlabeled samples. We compare learning in the semi-supervised model to the standard, ...
Shai Ben-David, Tyler Lu, Dávid Pál