Titlebook: Machine Learning and Knowledge Discovery in Databases; European Conference, Wray Buntine,Marko Grobelnik,John Shawe-Taylor Conference proce

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書目名稱Machine Learning and Knowledge Discovery in Databases影響因子(影響力)




書目名稱Machine Learning and Knowledge Discovery in Databases影響因子(影響力)學(xué)科排名




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書目名稱Machine Learning and Knowledge Discovery in Databases網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Machine Learning and Knowledge Discovery in Databases被引頻次




書目名稱Machine Learning and Knowledge Discovery in Databases被引頻次學(xué)科排名




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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 [1]. First, training is slower than other methods and linear SVMs, where recent advances in training algorithms

Infraction

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

商談

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