ML92 Proceedings of the ninth international workshop on Machine learning
Applications of machine learning: towards knowledge synthesis
Selected papers of international conference on Fifth generation computer systems 92
Applications of machine learning and rule induction
Communications of the ACM
Data mining
ReTAX: a step in the automation of taxonomic revision
Artificial Intelligence - Special issue on scientific discovery
Advances in knowledge discovery and data mining
Advances in knowledge discovery and data mining
Machine Learning - Special issue on applications of machine learning and the knowledge discovery process
Knowledge engineering and management: the CommonKADS methodology
Knowledge engineering and management: the CommonKADS methodology
Using inductive machine learning to support decision making in machining processes
Computers in Industry
Data mining: concepts and techniques
Data mining: concepts and techniques
Principles of data mining
Machine Learning and Data Mining; Methods and Applications
Machine Learning and Data Mining; Methods and Applications
Machine Learning and Its Applications: advanced lectures
Machine Learning and Its Applications: advanced lectures
The computational support of scientific discovery
Machine Learning and Its Applications
An Interactive Case-Based Reasoning Approach for Generating Expressive Music
Applied Intelligence
Data Mining and Knowledge Discovery
Data Mining and Knowledge Discovery
Data Mining for Measuring and Improving the Success of Web Sites
Data Mining and Knowledge Discovery
Problem Decomposition and the Learning of Skills
ECML '95 Proceedings of the 8th European Conference on Machine Learning
A Case Study in Loyality and Satisfaction Research
ECML '97 Proceedings of the 9th European Conference on Machine Learning
ILP Experiments in Detecting Traffic Problems
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Inducing Models of human Control Skills
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Bayesian Classification Trees with Overlapping Leaves Applied to Credit-Scoring
PAKDD '98 Proceedings of the Second Pacific-Asia Conference on Research and Development in Knowledge Discovery and Data Mining
Using Machine Learning to Understand Operator's Skill
IEA/AIE '02 Proceedings of the 15th international conference on Industrial and engineering applications of artificial intelligence and expert systems: developments in applied artificial intelligence
Sciences: environmental sciences
Handbook of data mining and knowledge discovery
In search of the Horowitz factor
AI Magazine
Ontological user profiling in recommender systems
ACM Transactions on Information Systems (TOIS)
Induction of comprehensible models for gene expression datasets by subgroup discovery methodology
Journal of Biomedical Informatics - Special issue: Biomedical machine learning
Machine learning in prognosis of the femoral neck fracture recovery
Artificial Intelligence in Medicine
Active subgroup mining: a case study in coronary heart disease risk group detection
Artificial Intelligence in Medicine
Machine learning for medical diagnosis: history, state of the art and perspective
Artificial Intelligence in Medicine
Machine learning for survival analysis: a case study on recurrence of prostate cancer
Artificial Intelligence in Medicine
Literature Mining: Towards Better Understanding of Autism
AIME '07 Proceedings of the 11th conference on Artificial Intelligence in Medicine
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The terminology of Machine Learning and Data Mining methods does not always allow a simple match between practical problems and methods. While some problems look similar from the user's point of view, but require different methods to be solved, some others look very different, yet they can be solved by applying the same methods and tools. Choosing appropriate Machine Learning methods for problem solving in practice is therefore largely a matter of experience and it is not realistic to expect a simple look-up table with matches between problems and methods. However, some guidelines can be given and a collection that summarizes other people's experience can also be helpful. A small number of definitions characterize the tasks that are performed by a large proportion of methods. Most of the variation in methods is concerned with differences in data types and algorithmic aspects of methods. In this paper, we summarize the main task types and illustrate how a wide variety of practical problems are formulated in terms of these tasks. The match between problems and tasks is illustrated with a collection of example applications with the aim of helping to express new practical problems as Machine Learning tasks. Some tasks can be decomposed into subtasks, allowing a wider variety of matches between practical problems and (combinations of) methods. We review the main principles for choosing between alternatives and illustrate this with a large collection of applications. We believe that this provides some guidelines.