Quantifying inductive bias: AI learning algorithms and Valiant's learning framework
Artificial Intelligence
Computational limitations on learning from examples
Journal of the ACM (JACM)
Computational learning theory: an introduction
Computational learning theory: an introduction
An introduction to computational learning theory
An introduction to computational learning theory
Characterizations of learnability for classes of {0, …, n}-valued functions
Journal of Computer and System Sciences
Fast discovery of association rules
Advances in knowledge discovery and data mining
Data mining, hypergraph transversals, and machine learning (extended abstract)
PODS '97 Proceedings of the sixteenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
Principles of data mining
Incremental Version-Space Merging: A General Framework for Concept Learning
Incremental Version-Space Merging: A General Framework for Concept Learning
Machine Learning
Levelwise Search and Borders of Theories in KnowledgeDiscovery
Data Mining and Knowledge Discovery
Local and Global Methods in Data Mining: Basic Techniques and Open Problems
ICALP '02 Proceedings of the 29th International Colloquium on Automata, Languages and Programming
Data Mining: Machine Learning, Statistics, and Databases
SSDBM '96 Proceedings of the Eighth International Conference on Scientific and Statistical Database Management
A Theory of Inductive Query Answering
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Statistics and data mining: intersecting disciplines
ACM SIGKDD Explorations Newsletter
Theoretical frameworks for data mining
ACM SIGKDD Explorations Newsletter
Version spaces: an approach to concept learning.
Version spaces: an approach to concept learning.
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The last decade has witnessed an impressive growth of Data Mining through algorithms and applications. Despite the advances, a computational theory of Data Mining is still largely outstanding. This paper discusses some aspects relevant to computation in Data Mining from the point of view of the Machine Learning theoretician. Computational techniques used in other fields that deal with learning from data, such as Statistics and Machine Learning, are potentially very relevant. However, the specifics of Data Mining are such that most often those techniques are not directly applicable but require to be re-cast and reanalysed within Data Mining starting from first principles. We illustrate this with a PAC-learnability analysis for a Data Mining-like task. We show that accounting for Data Mining specific requirements, such as inference of weak predictors and agnosticity assumptions, requires the generalisation of the classical PAC framework in novel ways.