Active learning for microarray data

Active Learning with Wavelets for Microarray Data

In Supervised Learning it is assumed that is straightforward to obtained labeled data. However, in reality labeled data can be scarce or expensive to obtain. Active Learning (AL) is a way to deal with the above problem by asking for the labels of the most “informative” data points. We propose a novel AL method based on wavelet analysis, which pertains especially to the large number of dimension...

cDNA Microarray Data Normalization

Machine Learning Methods for Microarray Data Analysis

As members of the Dissertation Committee, we certify that we have read the dis-sertation prepared by Prasad Amaresh Gabbur entitled Machine Learning Methods for Microarray Data Analysis and recommend that it be accepted as fulfilling the dissertation requirement for the Degree of Doctor of Philosophy. Final approval and acceptance of this dissertation is contingent upon the candidate's submissi...

Active Learning for Networked Data

We introduce a novel active learning algorithm for classification of network data. In this setting, training instances are connected by a set of links to form a network, the labels of linked nodes are correlated, and the goal is to exploit these dependencies and accurately label the nodes. This problem arises in many domains, including social and biological network analysis and document classif...

Learning Active Learning from Data

In this paper, we suggest a novel data-driven approach to active learning (AL). The key idea is to train a regressor that predicts the expected error reduction for a candidate sample in a particular learning state. By formulating the query selection procedure as a regression problem we are not restricted to working with existing AL heuristics; instead, we learn strategies based on experience fr...