AbstractThe quality of ranking determines the success or failure of information retrieval and the goal of ranking is to learn a real-valued ranking function that induces a ranking or ordering over an instance space. We focus on stability and generalization ability of ranking SVM for replacement case. The query-level stability of ranking SVM for replacement case and the generalization bounds for such ranking algorithm via query-level stability by changing one element in sample set are given
We propose a new ranking paradigm for relational databases called Structured Value Ranking (SVR). S...
Many machine learning technologies such as Support Vector Machines, Boosting, and Neural Networks ha...
Decision making is challenging when there is more than one criterion to consider. In such cases, it ...
AbstractThe quality of ranking determines the success or failure of information retrieval and the go...
AbstractThe quality of ranking determines the success or failure of information retrieval and the go...
This paper is concerned with the generaliza-tion ability of learning to rank algorithms for informat...
Abstract. In this paper, we propose a new method for learning to rank. ‘Ranking SVM ’ is a method fo...
The paper is concerned with applying learning to rank to document retrieval. Ranking SVM is a typica...
This paper studies the learning problem of ranking when one wishes not just to accurately predict pa...
With the explosive emergence of vertical search domains, applying the broad-based ranking model dire...
Since their introduction, ranking SVM models have become a powerful tool for training content-based ...
Abstract. This paper examines in detail an alternative ranking prob-lem for search engines, movie re...
International audienceThis paper addresses the problem of variable ranking for support vector regres...
Learning ranking (or preference) functions has become an important data mining task in recent years,...
We propose a new ranking paradigm for relational databases called Structured Value Ranking (SVR). SV...
We propose a new ranking paradigm for relational databases called Structured Value Ranking (SVR). S...
Many machine learning technologies such as Support Vector Machines, Boosting, and Neural Networks ha...
Decision making is challenging when there is more than one criterion to consider. In such cases, it ...
AbstractThe quality of ranking determines the success or failure of information retrieval and the go...
AbstractThe quality of ranking determines the success or failure of information retrieval and the go...
This paper is concerned with the generaliza-tion ability of learning to rank algorithms for informat...
Abstract. In this paper, we propose a new method for learning to rank. ‘Ranking SVM ’ is a method fo...
The paper is concerned with applying learning to rank to document retrieval. Ranking SVM is a typica...
This paper studies the learning problem of ranking when one wishes not just to accurately predict pa...
With the explosive emergence of vertical search domains, applying the broad-based ranking model dire...
Since their introduction, ranking SVM models have become a powerful tool for training content-based ...
Abstract. This paper examines in detail an alternative ranking prob-lem for search engines, movie re...
International audienceThis paper addresses the problem of variable ranking for support vector regres...
Learning ranking (or preference) functions has become an important data mining task in recent years,...
We propose a new ranking paradigm for relational databases called Structured Value Ranking (SVR). SV...
We propose a new ranking paradigm for relational databases called Structured Value Ranking (SVR). S...
Many machine learning technologies such as Support Vector Machines, Boosting, and Neural Networks ha...
Decision making is challenging when there is more than one criterion to consider. In such cases, it ...