T
Tony Martinez
Researcher at Brigham Young University
Publications - 185
Citations - 6958
Tony Martinez is an academic researcher from Brigham Young University. The author has contributed to research in topics: Artificial neural network & Instance-based learning. The author has an hindex of 32, co-authored 184 publications receiving 6175 citations. Previous affiliations of Tony Martinez include Lynn University.
Papers
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Improved heterogeneous distance functions
D. Randall Wilson,Tony Martinez +1 more
TL;DR: This article proposed three new heterogeneous distance functions, called the Heterogeneous Value Difference Metric (HVDM), the Interpolated Value Difference metric (IVDM), and the Windowed Value Difference measure (WVDM) to handle applications with nominal attributes, continuous attributes and both.
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Reduction Techniques for Instance-BasedLearning Algorithms
D. Randall Wilson,Tony Martinez +1 more
TL;DR: Of those algorithms that provide substantial storage reduction, the DROP algorithms have the highest average generalization accuracy in these experiments, especially in the presence of uniform class noise.
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The general inefficiency of batch training for gradient descent learning
D. Randall Wilson,Tony Martinez +1 more
TL;DR: Empirical results on a large (20,000-instance) speech recognition task and on 26 other learning tasks demonstrate that convergence can be reached significantly faster using on-line training than batch training, with no apparent difference in accuracy.
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An instance level analysis of data complexity
TL;DR: This paper identifies instances that are hard to classify correctly (instance hardness) by classifying over 190,000 instances from 64 data sets with 9 learning algorithms and finds that class overlap is a principal contributor to instance hardness.
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Quantum associative memory
Dan Ventura,Tony Martinez +1 more
TL;DR: This paper combines quantum computation with classical neural network theory to produce a quantum computational learning algorithm that produces an exponential increase in the capacity of the memory when compared to traditional associative memories such as the Hopfield network.