Machine learning an artificial intelligence approach volume II
Machine learning an artificial intelligence approach volume II
The Utility of Knowledge in Inductive Learning
Machine Learning
Neural networks and the bias/variance dilemma
Neural Computation
Original Contribution: Stacked generalization
Neural Networks
C4.5: programs for machine learning
C4.5: programs for machine learning
Machine Learning
Multistrategy Learning and Theory Revision
Machine Learning - Special issue on multistrategy learning
Theory refinement combining analytical and empirical methods
Artificial Intelligence
Prediction of generalization ability in learning machines
Prediction of generalization ability in learning machines
Knowledge-based artificial neural networks
Artificial Intelligence
An introduction to computational learning theory
An introduction to computational learning theory
Empirical methods for artificial intelligence
Empirical methods for artificial intelligence
The nature of statistical learning theory
The nature of statistical learning theory
Overfitting and undercomputing in machine learning
ACM Computing Surveys (CSUR)
Elements of machine learning
Machine learning, neural and statistical classification
Machine learning, neural and statistical classification
Evaluation and Selection of Biases in Machine Learning
Machine Learning - Special issue on bias evaluation and selection
Machine Learning
Unifying instance-based and rule-based induction
Machine Learning
Fast discovery of association rules
Advances in knowledge discovery and data mining
Lazy learning
On the Optimality of the Simple Bayesian Classifier under Zero-One Loss
Machine Learning - Special issue on learning with probabilistic representations
Self bounding learning algorithms
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Advances in kernel methods: support vector learning
Advances in kernel methods: support vector learning
Learning in graphical models
Efficient progressive sampling
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
MetaCost: a general method for making classifiers cost-sensitive
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Bayesian networks for lossless dataset compression
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Multiple Comparisons in Induction Algorithms
Machine Learning
Advances in Inductive Logic Programming
Advances in Inductive Logic Programming
Machine Learning
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Readings in Machine Learning
On Bias, Variance, 0/1—Loss, and the Curse-of-Dimensionality
Data Mining and Knowledge Discovery
A Survey of Methods for Scaling Up Inductive Algorithms
Data Mining and Knowledge Discovery
SLIQ: A Fast Scalable Classifier for Data Mining
EDBT '96 Proceedings of the 5th International Conference on Extending Database Technology: Advances in Database Technology
The Effects of Training Set Size on Decision Tree Complexity
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
SPRINT: A Scalable Parallel Classifier for Data Mining
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
Process-Oriented Estimation of Generalization Error
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
A Unified Bias-Variance Decomposition for Zero-One and Squared Loss
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Extracting comprehensible models from trained neural networks
Extracting comprehensible models from trained neural networks
The lack of a priori distinctions between learning algorithms
Neural Computation
Further experimental evidence against the utility of Occam's razor
Journal of Artificial Intelligence Research
Cached sufficient statistics for efficient machine learning with large datasets
Journal of Artificial Intelligence Research
Journal of Artificial Intelligence Research
Category translation: learning to understand information on the internet
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
Oversearching and layered search in empirical learning
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Lookahead and pathology in decision tree induction
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
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Machine learning's focus on ill-defined problems and highly flexible methods makes it ideally suited for knowledge discovery in databases (KDD) applications. Among the ideas machine learning contributes to KDD are the importance of empirical validation, the impossibility of learning without a priori assumptions, and the utility of limited-search or limited-representation methods. Machine learning provides methods for incorporating knowledge into the learning process, changing and combining representations, combatting the curse of dimensionality, and learning comprehensible models. KDD challenges for machine learning include scaling up its algorithms to large databases, using cost information in learning, automating data preprocessing, and enabling rapid development of applications. KDD opens up new directions for machine-learning research and brings new urgency to others. These directions include interfacing with the human user and the database system, learning from nonattribute-vector data, learning partial models, and learning continuously from an open-ended stream of data.