Machine Learning for the Detection of Oil Spills in Satellite Radar Images
Machine Learning - Special issue on applications of machine learning and the knowledge discovery process
Knowledge-Based Learning in Exploratory Science: Learning Rules to Predict Rodent Carcinogenicity
Machine Learning - Special issue on applications of machine learning and the knowledge discovery process
Ontology-guided knowledge discovery in databases
Proceedings of the 1st international conference on Knowledge capture
A Survey of Methods for Scaling Up Inductive Algorithms
Data Mining and Knowledge Discovery
Data Mining and Knowledge Discovery
Improved Dataset Characterisation for Meta-learning
DS '02 Proceedings of the 5th International Conference on Discovery Science
Collective, Hierarchical Clustering from Distributed, Heterogeneous Data
Revised Papers from Large-Scale Parallel Data Mining, Workshop on Large-Scale Parallel KDD Systems, SIGKDD
Data mining tasks and methods: scalability
Handbook of data mining and knowledge discovery
Using Rule Induction Methods to Analyze Gene Expression Data
CSB '03 Proceedings of the IEEE Computer Society Conference on Bioinformatics
IEEE Transactions on Knowledge and Data Engineering
Predicting dire outcomes of patients with community acquired pneumonia
Journal of Biomedical Informatics - Special issue: Clinical machine learning
How to shift bias: Lessons from the baldwin effect
Evolutionary Computation
METAL A: a Distributed System for Web Usage Mining
IWANN '03 Proceedings of the 7th International Work-Conference on Artificial and Natural Neural Networks: Part II: Artificial Neural Nets Problem Solving Methods
Anytime induction of low-cost, low-error classifiers: a sampling-based approach
Journal of Artificial Intelligence Research
Connectionist theory refinement: genetically searching the space of network topologies
Journal of Artificial Intelligence Research
Journal of Artificial Intelligence Research
CSNL: A cost-sensitive non-linear decision tree algorithm
ACM Transactions on Knowledge Discovery from Data (TKDD)
ILP'07 Proceedings of the 17th international conference on Inductive logic programming
Scaling up: distributed machine learning with cooperation
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Semantic translation for rule-based knowledge in data mining
DEXA'11 Proceedings of the 22nd international conference on Database and expert systems applications - Volume Part II
Rule learning for disease-specific biomarker discovery from clinical proteomic mass spectra
BioDM'06 Proceedings of the 2006 international conference on Data Mining for Biomedical Applications
Knowledge discovery using concept-class taxonomies
AI'04 Proceedings of the 17th Australian joint conference on Advances in Artificial Intelligence
Artificial Intelligence in Medicine
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This paper extends the currently accepted model of inductive bias by identifying six categories of bias and separates inductive bias from the policy for its selection (the inductive policy). We analyze existing “bias selection” systems, examining the similarities and differences in their inductive policies, and identify three techniques useful for building inductive policies. We then present a framework for representing and automatically selecting a wide variety of biases and describe experiments with an instantiation of the framework addressing various pragmatic tradeoffs of time, space, accuracy, and the cost of errors. The experiments show that a common framework can be used to implement policies for a variety of different types of bias selection, such as parameter selection, term selection, and example selection, using similar techniques. The experiments also show that different tradeoffs can be made by the implementation of different policies; for example, from the same data different rule sets can be learned based on different tradeoffs of accuracy versus the cost of erroneous predictions.