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Neural Networks
(1996-03-13)
We present an overview of current research on artificial neural networks, emphasizing a statistical perspective. We view neural networks as parameterized graphs that make probabilistic assumptions about data, and view ...
Learning World Models in Environments with Manifest Causal Structure
(1995-05-05)
This thesis examines the problem of an autonomous agent learning a causal world model of its environment. Previous approaches to learning causal world models have concentrated on environments that are too "easy" ...
Comparing Support Vector Machines with Gaussian Kernels to Radial Basis Function Classifiers
(1996-12-01)
The Support Vector (SV) machine is a novel type of learning machine, based on statistical learning theory, which contains polynomial classifiers, neural networks, and radial basis function (RBF) networks as special ...