Alpha seeding for support vector machines

International Conference on Knowledge Discovery and Data Mining (KDD-2000), August 2000
Alpha seeding for support vector machines
Dennis DeCoste, K. Wagstaff
Abstract

A key practical obstacle in applying support vector machines to many large-scale data mining tasks is that SVM’s generally scale quadratically (or worse) in the number of examples or support vectors.

This complexity is further compounded when a specific SVM training is but one of many, such as in Leave-One-Out-Cross-Validation (LOOCV) for determining optimal SVM kernel parameters or as in wrapper-based feature selection. In this paper we explore new techniques for reducing the amortized cost of each such SVM training, by seeding successive SVM trainings with the results of previous similar trainings.

Another publication from the same category: Machine Learning and Data Science

WWW '17 Perth Australia April 2017

Drawing Sound Conclusions from Noisy Judgments

David Goldberg, Andrew Trotman, Xiao Wang, Wei Min, Zongru Wan

The quality of a search engine is typically evaluated using hand-labeled data sets, where the labels indicate the relevance of documents to queries. Often the number of labels needed is too large to be created by the best annotators, and so less accurate labels (e.g. from crowdsourcing) must be used. This introduces errors in the labels, and thus errors in standard precision metrics (such as P@k and DCG); the lower the quality of the judge, the more errorful the labels, consequently the more inaccurate the metric. We introduce equations and algorithms that can adjust the metrics to the values they would have had if there were no annotation errors.

This is especially important when two search engines are compared by comparing their metrics. We give examples where one engine appeared to be statistically significantly better than the other, but the effect disappeared after the metrics were corrected for annotation error. In other words the evidence supporting a statistical difference was illusory, and caused by a failure to account for annotation error.

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