Capricious
发表于 2025-3-21 17:07:38
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Pamphlet
发表于 2025-3-21 23:26:11
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人造
发表于 2025-3-22 02:11:15
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Tdd526
发表于 2025-3-22 05:38:05
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GEAR
发表于 2025-3-22 11:15:58
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cleaver
发表于 2025-3-22 14:36:23
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patriot
发表于 2025-3-22 18:14:39
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Bravura
发表于 2025-3-22 23:00:33
Sparse Kernel SVMs via Cutting-Plane Training is often hindered by the following two problems. Both problems can be traced back to the number of Support Vectors (SVs), which is known to generally grow linearly with the data set size . First, training is slower than other methods and linear SVMs, where recent advances in training algorithms
Infraction
发表于 2025-3-23 01:41:29
Hybrid Least-Squares Algorithms for Approximate Policy Evaluation different choices of the optimization criterion. Two popular least-squares algorithms for performing this task are the . method, which minimizes the Bellman residual, and the . method, which minimizes the . of the Bellman residual. When used within policy iteration, the fixed point algorithm tends
商谈
发表于 2025-3-23 05:34:24
A Self-training Approach to Cost Sensitive Uncertainty Sampling such as loss-reduction methods. However, unlike loss-reduction methods, uncertainty sampling cannot minimize total misclassification costs when errors incur different costs. This paper introduces a method for performing cost-sensitive uncertainty sampling that makes use of self-training. We show th