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Data Mining and Knowledge Discovery Handbook, 2 Edition part 75

Data Mining and Knowledge Discovery Handbook, 2 Edition part 75. Knowledge Discovery demonstrates intelligent computing at its best, and is the most desirable and interesting end-product of Information Technology. To be able to discover and to extract knowledge from data is a task that many researchers and practitioners are endeavoring to accomplish. There is a lot of hidden knowledge waiting to be discovered – this is the challenge created by today’s abundance of data. Data Mining and Knowledge Discovery Handbook, 2nd Edition organizes the most current concepts, theories, standards, methodologies, trends, challenges and applications of data mining (DM) and knowledge discovery. | Fig. 36.1. The Knowledge-Acquisition Mode Databases Learning Techniques preprocessing parameter settings base learners C B Meta-Feature Generator D E Ricardo Vilalta Christophe Giraud-Carrier and Pavel Brazdil 36 Meta-Learning 721 e.g. learning model . This view is problematic as the meta-learner is now a learning system subject to improvement through meta-learning Schmidhuber 1995 Vilalta 2001 . Second the matching process is not intended to modify our set of available learning techniques but simply enables us to select one or more strategies that seem effective given the characteristics of the dataset under analysis. The final classifier or combination of classifiers Figure 36.2D is selected based not only on its generalization performance over the current dataset but also on information derived from exploiting past experience. In this case the system has moved from using a single learning strategy to the ability of selecting one dynamically from among a variety of different strategies. We will show how the constituent components conforming our two-mode meta-learning architecture can be studied and utilized through a variety of different methodologies 1. The characterization of datasets can be performed under a variety of statistical information-theoretic and model-based approaches Section 36.3.1 . 2. Matching meta-features to predictive model s can be used for model selection or model ranking Section 36.3.1 . 3. Information collected from the performance of a set of learning algorithms at the base level can be combined through a meta-learner Section 36.3.1 . 4. Within the learning-to-learn paradigm a continuous learner can extract knowledge across domains or tasks to accelerate the rate of learning convergence Section 36.3.1 . 5. The learning strategy can be modified in an attempt to shift this strategy dynamically Section 36.3.2 . A meta-learner in effect explores not only the space of hypotheses within a fixed family set but the space of families of .