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ICML
2009
IEEE
16 years 3 months ago
BoltzRank: learning to maximize expected ranking gain
Ranking a set of retrieved documents according to their relevance to a query is a popular problem in information retrieval. Methods that learn ranking functions are difficult to o...
Maksims Volkovs, Richard S. Zemel
ICML
2009
IEEE
16 years 3 months ago
Compositional noisy-logical learning
We describe a new method for learning the conditional probability distribution of a binary-valued variable from labelled training examples. Our proposed Compositional Noisy-Logica...
Alan L. Yuille, Songfeng Zheng
148
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ICML
2006
IEEE
16 years 3 months ago
Cost-sensitive learning with conditional Markov networks
There has been a recent, growing interest in classification and link prediction in structured domains. Methods such as conditional random fields and relational Markov networks sup...
Prithviraj Sen, Lise Getoor
ICML
2000
IEEE
16 years 3 months ago
Maximum Entropy Markov Models for Information Extraction and Segmentation
Hidden Markov models (HMMs) are a powerful probabilistic tool for modeling sequential data, and have been applied with success to many text-related tasks, such as part-of-speech t...
Andrew McCallum, Dayne Freitag, Fernando C. N. Per...
ICML
2000
IEEE
16 years 3 months ago
Constructive Feature Learning and the Development of Visual Expertise
We present a framework for learning features for visual discrimination. The learning system is exposed to a sequence of training images. Whenever it fails to recognize a visual co...
Justus H. Piater, Roderic A. Grupen