CSCE 496/896 Topic Survey/Implementation Ideas

Identifying genetic structures that possess certain proteins (implementation; has the possibility of becoming a research project, if desired)

Principal Component Analysis (PCA) for feature selection (report or implementation)

Learning in fuzzy systems, e.g. learning a neural network-based fuzzy controller for a network, a robot, or some other engineering system (report or implementation)

Learning in network control, especially call admission control (CAC) in an ATM network (report or implementation)

Automated computer system intrusion detection, i.e. automatically identifing when a person has cracked into a computer system (report or implementation; has the possibility of becoming a research project, if desired)

Learning to classify web pages or usenet news (report or implementation)

Learning to make content-based queries in a visual database (report)

Learning in multi-agent systems (report)

A study of multiple-instance learning, where each labeled example consists of several instances from the instance space, and we don't know which instance is responsible for the label (report, maybe implementation)

Using reinforcement learning for optimization problems such as TSP (report, maybe implementation)

A study of new results in boosting and bagging (report, maybe implementation)

Implementing a version of exponentiated gradient and comparing it to the gradient descent algorithm (implementation)

Multi-threshold perceptron (report or implementation)

Using exponentiated gradient in reinforcement learning (report or implementation)

Using expert-based algorithms (e.g. weighted majority) for pruning decision trees (report or implementation)

Using expert-based algorithms (e.g. weighted majority) for predicting disk accesses (report or implementation)

Using expert-based algorithms (e.g. weighted majority) for predicting the stock market or for rating movies (implementation)

Using statistical hypothesis testing methods for comparing classifiers (report or implementation)

Dealing with unlabeled data, e.g. clustering, the expectation maximization algorithm, or co-training (report or implementation)

Learning to learn (report or implementation)

Learning hidden Markov models [HMMs] (report or implementation)

Genetic algorithms running in constrained domains (report or implementation)

Hardware-based genetic algorithms (report)

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