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We propose a cost-effective system for the determination of metal ion concentration in water, addressing a central issue in water resources management. The system combines novel luminometric label array technology with a machine learning algorithm that selects a minimal number of array reagents (modulators) and liquid sample dilutions, such that enable accurate quantification. The algorithm is able...
We consider the problem of learning sparse linear models for multi-label prediction tasks under a hard constraint on the number of features. Such budget constraints are important in domains where the acquisition of the feature values is costly. We propose a greedy multi-label regularized least-squares algorithm that solves this problem by combining greedy forward selection search with a cross-validation...
Multi-task feature selection refers to the problem of selecting a common predictive set of features over multiple related learning tasks. The problem is encountered for example in applications, where one can afford only a limited set of feature extractors for solving several tasks. In this work, we present a regularized least-squares (RLS) based algorithm for multi-task greedy forward feature selection...
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