Approximately optimal subset selection for statistical design and modelling

Optimal Subset Selection for Active Learning

Active learning traditionally relies on instance based utility measures to rank and select instances for labeling, which may result in labeling redundancy. To address this issue, we explore instance utility from two dimensions: individual uncertainty and instance disparity, using a correlation matrix. The active learning is transformed to a semi-definite programming problem to select an optimal...

Approximately Optimal Teaching of Approximately Optimal Learners

We propose a method of generating teaching policies for use in intelligent tutoring systems (ITS) for concept learning tasks [37], e.g., teaching students the meanings of words by showing images that exemplify their meanings à la Rosetta Stone [30] and Duo Lingo [13]. The approach is grounded in control theory and capitalizes on recent work by [28], [29] that frames the “teaching” problem as th...

Feature Subset Selection for Software Cost Modelling and Estimation

Feature selection has been recently used in the area of software engineering for improving the accuracy and robustness of software cost models. The idea behind selecting the most informative subset of features from a pool of available cost drivers stems from the hypothesis that reducing the dimensionality of datasets will significantly minimise the complexity and time required to reach to an es...

Optimal Features Subset Selection and Classification for Iris Recognition

The selection of the optimal features subset and the classification have become an important issue in the field of iris recognition. We propose a feature selection scheme based on the multiobjectives genetic algorithm (MOGA) to improve the recognition accuracy and asymmetrical support vector machine for the classification of iris patterns. We also suggest a segmentation scheme based on the coll...

Statistical Experiment and Optimal Design