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JMLR
2006
124views more  JMLR 2006»
13 years 10 months ago
A Direct Method for Building Sparse Kernel Learning Algorithms
Many kernel learning algorithms, including support vector machines, result in a kernel machine, such as a kernel classifier, whose key component is a weight vector in a feature sp...
Mingrui Wu, Bernhard Schölkopf, Gökhan H...
ICML
2004
IEEE
14 years 3 months ago
Learning a kernel matrix for nonlinear dimensionality reduction
We investigate how to learn a kernel matrix for high dimensional data that lies on or near a low dimensional manifold. Noting that the kernel matrix implicitly maps the data into ...
Kilian Q. Weinberger, Fei Sha, Lawrence K. Saul
KDD
2010
ACM
235views Data Mining» more  KDD 2010»
14 years 2 months ago
New perspectives and methods in link prediction
This paper examines important factors for link prediction in networks and provides a general, high-performance framework for the prediction task. Link prediction in sparse network...
Ryan Lichtenwalter, Jake T. Lussier, Nitesh V. Cha...
GECCO
2009
Springer
162views Optimization» more  GECCO 2009»
14 years 2 months ago
On the appropriateness of evolutionary rule learning algorithms for malware detection
In this paper, we evaluate the performance of ten well-known evolutionary and non-evolutionary rule learning algorithms. The comparative study is performed on a real-world classiï...
M. Zubair Shafiq, S. Momina Tabish, Muddassar Faro...
NECO
2010
136views more  NECO 2010»
13 years 8 months ago
Learning to Represent Spatial Transformations with Factored Higher-Order Boltzmann Machines
To allow the hidden units of a restricted Boltzmann machine to model the transformation between two successive images, Memisevic and Hinton (2007) introduced three-way multiplicat...
Roland Memisevic, Geoffrey E. Hinton