CSCE 478/878 Topic Survey/Implementation Ideas
For a course project, more breadth is expected from a report (literature
reivew) than in an implementation. In both cases, a detailed description
of different approaches is expected, as well as a careful comparison
and a critical assessment.
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Principal Component Analysis (PCA) for feature selection (report or
implementation)
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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)
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Learning in network control, especially call admission control (CAC) in an
ATM network (report or implementation)
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Automated computer system intrusion detection, i.e.
automatically identifing when a person has cracked into a computer system
(report or implementation)
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Branch prediction to enhance program performance (computer architecture)
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Learning to classify web pages or usenet news (report or implementation)
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Learning to make content-based queries in a visual database (report)
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Learning in multi-agent systems (report)
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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)
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Using reinforcement learning for optimization problems such as TSP (report, maybe implementation)
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A study of new results in boosting and bagging (report, maybe implementation)
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Implementing a version of exponentiated gradient and comparing it to
the gradient descent algorithm (implementation)
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Multi-threshold perceptron (report or implementation)
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Using exponentiated gradient in reinforcement learning (report or implementation)
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Using expert-based algorithms (e.g. weighted majority) for pruning decision trees (report or implementation)
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Using expert-based algorithms (e.g. weighted majority) for predicting disk accesses (report or implementation)
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Using expert-based algorithms (e.g. weighted majority) for predicting the stock market or for rating
movies (implementation)
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Using statistical hypothesis testing methods for comparing classifiers
(report or implementation)
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Dealing with unlabeled data, e.g. clustering, the expectation maximization
algorithm, or co-training
(report or implementation)
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Learning to learn (report or implementation)
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Learning hidden Markov models [HMMs] (report or implementation)
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Genetic algorithms running in constrained domains
(report or implementation)
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Hardware-based genetic algorithms (report)
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