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Topic Id:
ID topic: 526
Partner Email: L.J.M.Rothkrantz@tudelft.nl
Project Title: EVOLUTIONARY OPTIMIZATION OF KERNEL MACHINES
Abstract: In this report we present an automated approach for finding a good kernel function and optimal hyperparameters using Evolutionary Computation techniques. Evolutionary Computation is class of optimization techniques that is inspired by biological evolution. Potential solutions to the problem under consideration are iteratively generated, mutated, recombined, and evaluated. The search process converges to good solutions, since it biases reproduction toward the fittest individuals (cf. \"survival of the fittest\"). Two separate evolutionary models are proposed in this report. The first of these two models uses Evolution Strategies to rapidly find optimal hyperparameters. The second model aims to improve the generalization capacity of the machine by evolving complex kernel functions using Genetic Programming. Empirical studies show that our Evolution Strategies approach is able to find competitive hyperparameters, as compared with traditional methods, in less time for regression problems. Classification problems are problematic, due to the discontinuity of the error surface. Nonetheless, it has to be noted that the approach is still able to find reasonable solutions in a time efficient manner. The Genetic Programming approach, however, is shown to improve the generalization capacity of the machine only marginally. In most practical applications this minor improvement will not justify the high computational requirements of the model. Lastly, we present a study on the reliability of Kernel Target Alignment as a performance measure for Kernel Machines. The main advantage of Kernel Target Alignment is the computational efficiency, as compared with other performance measures. Nonetheless, our empirical study suggests that Kernel Target Alignment does not reliably approximate the true generalization performance of a Kernel Machine. Therefore, the use of this measure as an objective function should be avoided.
Advisor: Leon Rothkrantz
Link:
Degree: Master
 Keywords:
Computer Software
Algorithms & problem solving
Artificial intelligence & Neural networks
Automata & state machines
Data mining
Data modeling