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» Machine Learning by Function Decomposition
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IJCNN
2007
IEEE
15 years 6 months ago
Agnostic Learning versus Prior Knowledge in the Design of Kernel Machines
Abstract— The optimal model parameters of a kernel machine are typically given by the solution of a convex optimisation problem with a single global optimum. Obtaining the best p...
Gavin C. Cawley, Nicola L. C. Talbot
ICML
2001
IEEE
16 years 18 days ago
Off-Policy Temporal Difference Learning with Function Approximation
We introduce the first algorithm for off-policy temporal-difference learning that is stable with linear function approximation. Off-policy learning is of interest because it forms...
Doina Precup, Richard S. Sutton, Sanjoy Dasgupta
COCO
2010
Springer
149views Algorithms» more  COCO 2010»
15 years 2 months ago
The Gaussian Surface Area and Noise Sensitivity of Degree-d Polynomial Threshold Functions
Abstract. We prove asymptotically optimal bounds on the Gaussian noise sensitivity of degree-d polynomial threshold functions. These bounds translate into optimal bounds on the Gau...
Daniel M. Kane
ML
2002
ACM
163views Machine Learning» more  ML 2002»
14 years 11 months ago
Structural Modelling with Sparse Kernels
A widely acknowledged drawback of many statistical modelling techniques, commonly used in machine learning, is that the resulting model is extremely difficult to interpret. A numb...
Steve R. Gunn, Jaz S. Kandola
CVPR
2010
IEEE
15 years 8 months ago
Learning Shift-Invariant Sparse Representation of Actions
A central problem in the analysis of motion capture (Mo- Cap) data is how to decompose motion sequences into primitives. Ideally, a description in terms of primitives should fac...
Yi Li