1413 search results - page 163 / 283 » A Theory for Memory-Based Learning |
147
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ALT
2004
Springer
16 years 3 months ago
2004
Springer
Rademacher and Gaussian complexities are successfully used in learning theory for measuring the capacity of the class of functions to be learned. One of the most important propert...
153
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COLT
2007
Springer
16 years 15 days ago
2007
Springer
For the natural notion of splitting classes into two disjoint subclasses via a recursive classifier working on texts, the question is addressed how these splittings can look in th...
186
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AIIA
2003
Springer
15 years 11 months ago
2003
Springer
A bayesian network is an appropriate tool for working with uncertainty and probability, that are typical of real-life applications. In literature we find different approaches for b...
195
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COLT
2000
Springer
15 years 10 months ago
2000
Springer
We describe a novel family of PAC model algorithms for learning linear threshold functions. The new algorithms work by boosting a simple weak learner and exhibit complexity bounds...
159
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ECML
1993
Springer
15 years 10 months ago
1993
Springer
In machine learning theory, problem classes are distinguished because of di erences in complexity. In 6 , a stochastic model of learning from examples was introduced. This PAClear...