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» On learning with dissimilarity functions
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NIPS
2003
15 years 8 months ago
Learning the k in k-means
When clustering a dataset, the right number k of clusters to use is often not obvious, and choosing k automatically is a hard algorithmic problem. In this paper we present an impr...
Greg Hamerly, Charles Elkan
184
Voted
AAAI
1998
15 years 8 months ago
Learning to Classify Text from Labeled and Unlabeled Documents
In many important text classification problems, acquiring class labels for training documents is costly, while gathering large quantities of unlabeled data is cheap. This paper sh...
Kamal Nigam, Andrew McCallum, Sebastian Thrun, Tom...
IAT
2008
IEEE
15 years 7 months ago
Structured Learning of Component Dependencies in AmI Systems
As information and communication technologies are becoming an integral part of our homes, the demand for AmI systems with assistive functionality is increasing. A great effort has...
Todor Dimitrov, Josef Pauli, Edwin Naroska, Christ...
PAMI
2008
135views more  PAMI 2008»
15 years 6 months ago
MultiK-MHKS: A Novel Multiple Kernel Learning Algorithm
In this paper, we develop a new effective multiple kernel learning algorithm. First, we map the input data into m different feature spaces by m empirical kernels, where each genera...
Zhe Wang, Songcan Chen, Tingkai Sun
NN
2006
Springer
127views Neural Networks» more  NN 2006»
15 years 6 months ago
The asymptotic equipartition property in reinforcement learning and its relation to return maximization
We discuss an important property called the asymptotic equipartition property on empirical sequences in reinforcement learning. This states that the typical set of empirical seque...
Kazunori Iwata, Kazushi Ikeda, Hideaki Sakai