Fast query-optimized kernel machine classification via incremental approximate nearest support vectors

International Conference on Machine Learning (ICML), August 2003
Fast query-optimized kernel machine classification via incremental approximate nearest support vectors
Dennis DeCoste, D. Mazzoni, Dennis DeCoste, D. Mazzoni

Support vector machines (and other ker-nel machines) offer robust modern machine learning methods for nonlinear classification. However, relative to other alternatives (such as linear methods, decision trees and neu-ral networks), they can be orders of mag-nitude slower at query-time.

Unlike exist-ing methods that attempt to speedup query-time, such as reduced set compression (e.g. (Burges, 1996)) and anytime bounding (e.g. (DeCoste, 2002), we propose a new and ef-ficient approach based on treating the ker-nel machine classifier as a special form of k nearest-neighbor.

Our approach improves upon a traditional k-NN by determining at query-time a good k for each query, based on pre-query analysis guided by the origi-nal robust kernel machine. We demonstrate effectiveness on high-dimensional benchmark MNIST data, observing a greater than 100-fold reduction in the number of SVs required per query (amortized over all 45 pairwise MNIST digit classifiers), with no extra test errors (in fact, it happens to make 4 fewer)

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.