Machine Learning Thesis

It is closely related to several existing principles used in machine learning such as Occam's razor, the minimum description length, and the Bayesian approach.

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This research-led approach shapes the way they educate students through teaching that opens everything up to question.

It’s a style of learning that relies on learning by discovery and prepares graduates to bring fresh perspectives to the ever-evolving landscape of technology.

Experiments were carried out with a practical complexity measure on several toy problems.

We then propose a family of novel learning algorithms to directly minimize the 0/1 loss for perceptrons.

We provide a more sophisticated abstract boosting algorithm, CGBoost, based on conjugate gradient in function space.

When the Ada Boost exponential cost function is optimized, CGBoost generally yields much lower cost and training error but higher test error, which implies that the exponential cost is vulnerable to overfitting.

A perceptron is a linear threshold classifier that separates examples with a hyperplane.

Unlike most perceptron learning algorithms, which require smooth cost functions, our algorithms directly minimize the 0/1 loss, and usually achieve the lowest training error compared with other algorithms. Such advantages make them favorable for both standalone use and ensemble learning, on problems that are not linearly separable.

The title of this degree programme and the credential awarded for its successful completion is subject to final approval by Imperial College London.

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