C4.5: programs for machine learning
C4.5: programs for machine learning
Artificial intelligence and molecular biology
Artificial intelligence and molecular biology
Speech Communication - Special issue: Fujisaki's Festschrift
Machine discovery in chemistry: new results
Artificial Intelligence
A new theorem in particle physics enabled by machine discovery
Artificial Intelligence
Unifying instance-based and rule-based induction
Machine Learning
Principles of human-computer collaboration for knowledge discovery in science
Artificial Intelligence
Separate-and-Conquer Rule Learning
Artificial Intelligence Review
Robust Classification for Imprecise Environments
Machine Learning
Clustering Algorithms
Learning Logical Definitions from Relations
Machine Learning
In Search of the Horowitz Factor: Interim Report on a Musical Discovery Project
DS '02 Proceedings of the 5th International Conference on Discovery Science
Rule-based machine learning methods for functional prediction
Journal of Artificial Intelligence Research
In search of the Horowitz factor
AI Magazine
Automatic identification of music performers with learning ensembles
Artificial Intelligence
Relational IBL in classical music
Machine Learning
From driving to expressive music performance: ensuring tempo smoothness
Proceedings of the 2006 ACM SIGCHI international conference on Advances in computer entertainment technology
From driving to expressive music performance: ensuring tempo smoothness
Proceedings of the 2006 ACM SIGCHI international conference on Advances in computer entertainment technology
Guest editorial: Machine learning in and for music
Machine Learning
Music compositional intelligence with an affective flavor
Proceedings of the 12th international conference on Intelligent user interfaces
Tonal Harmony Analysis: A Supervised Sequential Learning Approach
AI*IA '07 Proceedings of the 10th Congress of the Italian Association for Artificial Intelligence on AI*IA 2007: Artificial Intelligence and Human-Oriented Computing
A survey of computer systems for expressive music performance
ACM Computing Surveys (CSUR)
Automatic identification of music performers with learning ensembles
Artificial Intelligence
An emotion-driven musical piece generator for a constructive adaptive user interface
PRICAI'06 Proceedings of the 9th Pacific Rim international conference on Artificial intelligence
Playing Mozart phrase by phrase
ICCBR'03 Proceedings of the 5th international conference on Case-based reasoning: Research and Development
A state of the art on computational music performance
Expert Systems with Applications: An International Journal
Probabilistic and logic-based modelling of harmony
CMMR'10 Proceedings of the 7th international conference on Exploring music contents
A CSP approach for modeling the hand gestures of a virtual guitarist
AI*IA'05 Proceedings of the 9th conference on Advances in Artificial Intelligence
Extracting emotions from music data
ISMIS'05 Proceedings of the 15th international conference on Foundations of Intelligent Systems
Analyzing musical expressivity with a soft computing approach
Fuzzy Sets and Systems
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This article presents a new rule discovery algorithm named PLCG that can find simple, robust partial rule models (sets of classification rules) in complex data where it is difficult or impossible to find models that completely account for all the phenomena of interest. Technically speaking, PLCG is an ensemble learning method that learns multiple models via some standard rule learning algorithm, and then combines these into one final rule set via clustering, generalization, and heuristic rule selection. The algorithm was developed in the context of an interdisciplinary research project that aims at discovering fundamental principles of expressive music performance from large amounts of complex real-world data (specifically, measurements of actual performances by concert pianists). It will be shown that PLCG succeeds in finding some surprisingly simple and robust performance principles, some of which represent truly novel and musically meaningful discoveries. A set of more systematic experiments shows that PLCG usually discovers significantly simpler theories than more direct approaches to rule learning (including the state-of-the-art learning algorithm RIPPER), while striking a compromise between coverage and precision. The experiments also show how easy it is to use PLCG as a meta-learning strategy to explore different parts of the space of rule models.