Machine Learning and Algorithms

International audience

This paper is concerned with the class imbalance problem which has been known to hinder the learning performance of classification algorithms. The problem occurs when there are significantly less number of observations of the target concept. Various real-world classification tasks, such as medical diagnosis, text cate…

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• İLKAY ULUSOY

We consider the problem of evaluating retrieval systems using incomplete judgment information. Buckley and Voorhees recently demonstrated that retrieval systems can be efficiently and effectively evaluated using incomplete judgments via the bpref measure [6]. When relevance judgments are complete, the value of bpref i…

A fundamental question in information theory and in computer science is how to measure similarity or the amount of shared information between two sequences. We have proposed a metric, based on Kolmogorov complexity, to answer this question and have proven it to be universal. We apply this metric in measuring the amoun…

We consider the problem of large scale retrieval evaluation. Recently two methods based on random sampling were proposed as a solution to the extensive effort required to judge tens of thousands of documents. While the first method proposed by Aslam et al. [1] is quite accurate and efficient, it is overly complex, mak…

We consider the problem of large-scale retrieval evaluation, and we propose a statistical method for evaluating retrieval systems using incomplete judgments. Unlike existing techniques that (1) rely on effectively complete, and thus prohibitively expensive, relevance judgment sets, (2) produce biased estimates of stan…

The class imbalance problem has been known to hinder the learning performance of classification algorithms. Various real-world classification tasks such as text categorization suffer from this phenomenon. We demonstrate that active learning is capable of solving the problem.

In this paper, we propose a nonconvex online Support Vector Machine (SVM) algorithm (LASVM-NC) based on the Ramp Loss, which has the strong ability of suppressing the influence of outliers. Then, again in the online learning setting, we propose an outlier filtering mechanism (LASVM-I) based on approximating nonconvex…

• ŞEYDA ERTEKİN BOLELLİ

Machine learning is a science which was found and developed as a subfield of artificial intelligence in the 1950s. The first steps of machine learning goes back to the 1950s but there were no significant researches and developments on this science. However, in the 1990s, the researches on this field restarted, develop…

• ÖZER ÇELİK

Evaluation metrics play a critical role both in the context of comparative evaluation of the performance of retrieval systems and in the context of learning-to-rank (LTR) as objective functions to be optimized. Many different evaluation metrics have been proposed in the IR literature, with average precision (AP) being…

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• TONGUÇ ÜNLÜYURT