loading
Papers

Research.Publish.Connect.

Paper

Paper Unlock

Authors: Timo Reuter and Philipp Cimiano

Affiliation: CITEC and Universität Bielefeld, Germany

ISBN: 978-989-8425-79-9

Keyword(s): Clustering/classification, Machine learning, Similarity function, Event identification,Weight adjustment, Support vector machine, Data mining.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Clustering and Classification Methods ; Computational Intelligence ; Evolutionary Computing ; Knowledge Discovery and Information Retrieval ; Knowledge-Based Systems ; Machine Learning ; Soft Computing ; Symbolic Systems

Abstract: Every clustering algorithm requires a similarity measure, ideally optimized for the task in question. In this paper we are concerned with the task of identifying events in social media data and address the question of how a suitable similarity function can be learned from training data for this task. The task consists essentially in grouping social media documents by the event they belong to. In order to learn a similarity measure using machine learning techniques, we extract relevant events from last.fm and match the unique machine tags for these events to pictures uploaded to Flickr, thus getting a gold standard were each picture is assigned to its corresponding event. We evaluate the similarity measure with respect to accuracy on the task of assigning a picture to its correct event. We use SVMs to train an appropriate similarity measure and investigate the performance of different types of SVMs (Ranking SVMs vs. Standard SVMs), different strategies for creating training data as wel l as the impact of the amount of training data and the kernel used. Our results show that a suitable similarity measure can be learned from a few examples only given a suitable strategy for creating training data. We also show that i) Ranking SVMs can learn from fewer examples, ii) are more robust compared to standard SVMs in the sense that their performance does not vary significantly for different sizes and samples of training data and iii) are not as prone to overfitting as standard SVMs. (More)

PDF ImageFull Text

Download
CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 35.175.191.168

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
Reuter, T. and Cimiano, P. (2011). LEARNING SIMILARITY FUNCTIONS FOR EVENT IDENTIFICATION USING SUPPORT VECTOR MACHINES.In Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2011) ISBN 978-989-8425-79-9, pages 200-207. DOI: 10.5220/0003654602080215

@conference{kdir11,
author={Timo Reuter. and Philipp Cimiano.},
title={LEARNING SIMILARITY FUNCTIONS FOR EVENT IDENTIFICATION USING SUPPORT VECTOR MACHINES},
booktitle={Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2011)},
year={2011},
pages={200-207},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003654602080215},
isbn={978-989-8425-79-9},
}

TY - CONF

JO - Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2011)
TI - LEARNING SIMILARITY FUNCTIONS FOR EVENT IDENTIFICATION USING SUPPORT VECTOR MACHINES
SN - 978-989-8425-79-9
AU - Reuter, T.
AU - Cimiano, P.
PY - 2011
SP - 200
EP - 207
DO - 10.5220/0003654602080215

Login or register to post comments.

Comments on this Paper: Be the first to review this paper.