Year: 1994
Type: dissertation
Abstract: Model selection can be thought of as meta-learningit is the problem of finding the best model among a group of learning methods.To help avoid overfitting, this is normally done by cross validation.However, cross validation is computationally intensive, especially if the number of models or the number of training points is high.Methods such as gradient descent have problems such as local minima, or even worse, are not even applicable in some model spaces.In this thesis, I will develop a technique called Hoeffding Races for quickly discarding bad models and concentrating the computational effort at differentiating between the better ones, thereby reducing the number of cross validation queries.In addition, I will apply this technique to the problem of segmenting 3D magnetic resonance brain images and MS lesion detection.Despite the huge amount of data, I will show that learning algorithms such as memory based methods (variants of nearest-neighbor) can cope with problems that have required traditional vision techniques in the past.
Author: Oded Maron
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Awards: G7407884558, G8876996369
OpenAlex ID: W2225258192