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» On learning with dissimilarity functions
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ICML
2009
IEEE
16 years 7 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
2002
IEEE
16 years 7 months ago
Reinforcement Learning and Shaping: Encouraging Intended Behaviors
We explore dynamic shaping to integrate our prior beliefs of the final policy into a conventional reinforcement learning system. Shaping provides a positive or negative artificial...
Adam Laud, Gerald DeJong
161
Voted
IJCNN
2008
IEEE
16 years 17 days ago
On the learning of nonlinear visual features from natural images by optimizing response energies
— The operation of V1 simple cells in primates has been traditionally modelled with linear models resembling Gabor filters, whereas the functionality of subsequent visual cortic...
Jussi T. Lindgren, Aapo Hyvärinen
191
Voted
ECML
2004
Springer
15 years 10 months ago
Efficient Hyperkernel Learning Using Second-Order Cone Programming
The kernel function plays a central role in kernel methods. Most existing methods can only adapt the kernel parameters or the kernel matrix based on empirical data. Recently, Ong e...
Ivor W. Tsang, James T. Kwok
168
Voted
ICML
2010
IEEE
15 years 7 months ago
Gaussian Processes Multiple Instance Learning
This paper proposes a multiple instance learning (MIL) algorithm for Gaussian processes (GP). The GP-MIL model inherits two crucial benefits from GP: (i) a principle manner of lea...
Minyoung Kim, Fernando De la Torre