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» On Kernel Methods for Relational Learning
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IJCV
2008
266views more  IJCV 2008»
15 years 3 months ago
Learning to Recognize Objects with Little Supervision
This paper shows (i) improvements over state-of-the-art local feature recognition systems, (ii) how to formulate principled models for automatic local feature selection in object c...
Peter Carbonetto, Gyuri Dorkó, Cordelia Sch...
128
Voted
JMLR
2010
119views more  JMLR 2010»
14 years 10 months ago
Semi-Supervised Learning via Generalized Maximum Entropy
Various supervised inference methods can be analyzed as convex duals of the generalized maximum entropy (MaxEnt) framework. Generalized MaxEnt aims to find a distribution that max...
Ayse Erkan, Yasemin Altun
136
Voted
CVPR
2001
IEEE
16 years 5 months ago
Small Sample Learning during Multimedia Retrieval using BiasMap
All positive examples are alike; each negative example is negative in its own way. During interactive multimedia information retrieval, the number of training samples fed-back by ...
Xiang Sean Zhou, Thomas S. Huang
ICML
2004
IEEE
16 years 4 months ago
Unifying collaborative and content-based filtering
Collaborative and content-based filtering are two paradigms that have been applied in the context of recommender systems and user preference prediction. This paper proposes a nove...
Justin Basilico, Thomas Hofmann
157
Voted
ICML
2010
IEEE
15 years 4 months ago
Learning Sparse SVM for Feature Selection on Very High Dimensional Datasets
A sparse representation of Support Vector Machines (SVMs) with respect to input features is desirable for many applications. In this paper, by introducing a 0-1 control variable t...
Mingkui Tan, Li Wang, Ivor W. Tsang