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Yoav Freund works on applications of machine learning algorithms in bioinformatics, computer vision, finance, network routing and high-performance computing. He has developed a new approach to the study and development of machine learning algorithms, where the goal is to produce a good decision algorithm for a repetitive decision task. A decision algorithm receives as input an instance (sensory data) and outputs a decision (an action). After the decision has been made, there is a measurable outcome. Freund's main focus is on binary classification tasks, where the decision is binary, the outcome is binary, and the loss is 1 if the decision and outcome don't match and 0 if they do. Given these definitions and a source of instances and outcomes, Freund can evaluate the performance of any decision algorithm--treating it as a "black box". The practical advantage of the black-box approach is that it provides a measuring stick for comparing all types of decision algorithms, regardless of how they are constructed or analyzed. By extension, this approach produces a way of comparing all types of learning algorithms.
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arxiv(2024)
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bioRxiv (Cold Spring Harbor Laboratory) (2024)
CoRR (2023)
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