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» More generality in efficient multiple kernel learning
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ML
2008
ACM
110views Machine Learning» more  ML 2008»
13 years 7 months ago
A theory of learning with similarity functions
Kernel functions have become an extremely popular tool in machine learning, with an attractive theory as well. This theory views a kernel as implicitly mapping data points into a ...
Maria-Florina Balcan, Avrim Blum, Nathan Srebro
ICTAI
1994
IEEE
14 years 27 days ago
NSK, an Object-Oriented Simulator Kernel for Arbitrary Feedforward Neural Networks
An object-oriented neural network simulator kernel is presented. It is based on a general mathematical model for arbitrary feedforward nets. We propose a C++ implementation of thi...
Cédric Gégout, Bernard Girau, Fabric...
CVPR
2011
IEEE
1473views Computer Vision» more  CVPR 2011»
13 years 5 months ago
Object Recognition with Hierarchical Kernel Descriptors
Kernel descriptors provide a unified way to generate rich visual feature sets by turning pixel attributes into patch-level features, and yield impressive results on many object rec...
Liefeng Bo, Kevin Lai, Xiaofeng Ren and Dieter Fox
CORR
2010
Springer
124views Education» more  CORR 2010»
13 years 8 months ago
Online Learning of Noisy Data with Kernels
We study online learning when individual instances are corrupted by adversarially chosen random noise. We assume the noise distribution is unknown, and may change over time with n...
Nicolò Cesa-Bianchi, Shai Shalev-Shwartz, O...
ILP
2003
Springer
14 years 2 months ago
Graph Kernels and Gaussian Processes for Relational Reinforcement Learning
RRL is a relational reinforcement learning system based on Q-learning in relational state-action spaces. It aims to enable agents to learn how to act in an environment that has no ...
Thomas Gärtner, Kurt Driessens, Jan Ramon