Properties of Datasets Predict the Performance of Classifiers

Aghazadeh, Omid,1982-KTH,Datorseende och robotik, CVAP,Computer Vision Group (author)

QC 20140602
It has been shown that the performance of classifiers depends not only on the number of training samples, but also on the quality of the training set [10, 12]. The purpose of this paper is to 1) provide quantitative measures that determine the quality of the training set and 2) provide the relation between the test performance and the proposed measures. The measures are derived from pairwise affinities between training exemplars of the positive class and they have a generative nature. We show that the performance of the state of the art methods, on the test set, can be reasonably predicted based on the values of the proposed measures on the training set. These measures open up a wide range of applications to the recognition community enabling us to analyze the behavior of the learning algorithms w.r.t the properties of the training data. This will in turn enable us to devise rules for the automatic selection of training data that maximize the quantified quality of the training set and thereby improve recognition performance.

NATURVETENSKAP Data- och informationsvetenskap Datorseende och robotik hsv//swe
NATURAL SCIENCES Computer and Information Sciences Computer Vision and Robotics hsv//eng
Computer vision
Automatic selection
Performance of classifier
Quantitative measures
State-of-the-art methods
Test performance
Training data
Training sample
Training sets

Carlsson, StefanKTH,Datorseende och robotik, CVAP,Computer Vision Group(Swepub:kth)u1agqz5q (author)
KTHDatorseende och robotik, CVAP (creator_code:org_t)

In:BMVC 2013 - Electronic Proceedings of the British Machine Vision Conference 2013: British Machine Vision Association, BMVA

About the subject

NATURAL SCIENCES: NATURAL SCIENCES; and Computer and Inf ...; and Computer Vision ...

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