Proceedings Article10.1109/ICDM.2002.1184019
Learning from order examples
Toshihiro Kamishima,Shotaro Akaho +1 more
- 09 Dec 2002
- pp 645-648
TL;DR: A new learning task is advocated that deals with orders of items, and the aim is to acquire the rule that is used for estimating the proper order of a given unordered item set from training examples that are ordered item sets.
read more
Abstract: We advocate a new learning task that deals with orders of items, and we call this the learning from order examples (LOE) task. The aim of the task is to acquire the rule that is used for estimating the proper order of a given unordered item set. The rule is acquired from training examples that are ordered item sets. We present several solution methods for this task, and evaluate the performance and the characteristics of these methods based on the experimental results of tests using both artificial data and realistic data.
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
Machine learning
TL;DR: Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis.
How to rank with few errors
Claire Kenyon-Mathieu,Warren Schudy +1 more
- 11 Jun 2007
TL;DR: A polynomial time approximation scheme (PTAS) for the minimum feedback arc set problem on tournaments and a simple weighted generalization gives a PTAS for Kemeny-Young rank aggregation.
Nantonac collaborative filtering: recommendation based on order responses
Toshihiro Kamishima
- 24 Aug 2003
TL;DR: This work proposes some methods to recommed items based on these order responses, and carries out the comparison experiments of these methods.
Gene Expression Data Analysis Using a Novel Approach to Biclustering Combining Discrete and Continuous Data
TL;DR: This work uses the results of biclustering on discrete data as a starting point for a local search function on continuous data and presents results on random and real datasets that show the ability of the algorithm to capture statistically significant and biologically relevant biclusters.
14
Ranking Sentences for Keyphrase Extraction: A Relational Data Mining Approach☆
Michelangelo Ceci,Corrado Loglisci,Lucrezia Macchia +2 more
- 01 Jan 2014
TL;DR: A probabilistic relational data mining method is presented to model preference relations on sentences of document images and this is used to rank the sentences which will form the final summary.
10
References
Machine learning
TL;DR: Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis.
•Book
Rank correlation methods
Maurice G. Kendall
- 01 Jan 1948
TL;DR: The measurement of rank correlation was introduced in this paper, and rank correlation tied ranks tests of significance were applied to the problem of m ranking, and variate values were used to measure rank correlation.
6.6K
Learning to order things
TL;DR: An on-line algorithm for learning preference functions that is based on Freund and Schapire's "Hedge" algorithm is considered, and it is shown that the problem of finding the ordering that agrees best with a learned preference function is NP-complete.
Learning to Order Things
TL;DR: In this paper, the problem of learning how to order instances given feedback in the form of preference judgments, i.e., statements to the effect that one instance should be ranked ahead of another, is considered.
Related Papers (5)
Toshihiro Kamishima,Shotaro Akaho +1 more
- 01 Jan 2002
Cynthia Dwork,Ravi Kumar,Moni Naor,Dandapani Sivakumar +3 more
- 01 Apr 2001