Spelling checkers,spelling correctors and the misspellings of poor spellers
Information Processing and Management: an International Journal
Some advances in transformation-based part of speech tagging
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
Learning and making decisions when costs and probabilities are both unknown
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
A comparative study on content-based music genre classification
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
The Journal of Machine Learning Research
Style mining of electronic messages for multiple authorship discrimination: first results
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Automatic text categorization in terms of genre and author
Computational Linguistics
Semisupervised learning from different information sources
Knowledge and Information Systems
Learning word meanings and descriptive parameter spaces from music
HLT-NAACL-LWM '04 Proceedings of the HLT-NAACL 2003 workshop on Learning word meaning from non-linguistic data - Volume 6
Semi-supervised learning for music artists style identification
Proceedings of the thirteenth ACM international conference on Information and knowledge management
On combining multiple clusterings
Proceedings of the thirteenth ACM international conference on Information and knowledge management
A novel framework for efficient automated singer identification in large music databases
ACM Transactions on Information Systems (TOIS)
Computational creativity tools for songwriters
CALC '10 Proceedings of the NAACL HLT 2010 Second Workshop on Computational Approaches to Linguistic Creativity
On combining multiple clusterings: an overview and a new perspective
Applied Intelligence
Classification accuracy is not enough
Journal of Intelligent Information Systems
Hi-index | 0.00 |
Efficient and intelligent music information retrieval is a very important topic of the 21st century. With the ultimate goal of building personal music information retrieval systems, this paper studies the problem of identifying "similar" artists using both lyrics and acoustic data. The approach for using a small set of labeled samples for the seed labeling to build classifiers that improve themselves using unlabeled data is presented. This approach is tested on a data set consisting of 43 artists and 56 albums using artist similarity provided by All Music Guide. Experimental results show that using such an approach the accuracy of artist similarity classifiers can be significantly improved and that artist similarity can be efficiently identified.