A Model for Analysis of Computational Learning, M.Sc. Thesis Sharif University of Technology ; Daneshgar, Amir (Supervisor)
Abstract
The subject of this thesis is about a certain set of algorithms that try to indirectly solve problems. Instead of a programmer crafting an algorithm to solve a problem, these algorithms learn a solution themselves. These methods are usually studied in the Probably Accurately Correct (PAC) learning model. Although, PAC Learning is a generally accepted model, it falls short to describe certain aspects of learning algorithms. Many learning methods rely on convergence to minimize error, or maximize their fitness, yet the PAC model doesn’t explicitly provide any means to measure these behaviours. In this thesis, we first go through different models related to computational requirements of...
Cataloging briefA Model for Analysis of Computational Learning, M.Sc. Thesis Sharif University of Technology ; Daneshgar, Amir (Supervisor)
Abstract
The subject of this thesis is about a certain set of algorithms that try to indirectly solve problems. Instead of a programmer crafting an algorithm to solve a problem, these algorithms learn a solution themselves. These methods are usually studied in the Probably Accurately Correct (PAC) learning model. Although, PAC Learning is a generally accepted model, it falls short to describe certain aspects of learning algorithms. Many learning methods rely on convergence to minimize error, or maximize their fitness, yet the PAC model doesn’t explicitly provide any means to measure these behaviours. In this thesis, we first go through different models related to computational requirements of...
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