DATA MINING Desktop Survival Guide by Graham Williams
Desktop Survival Project Home List of Figures List of Tables Data Mining Data Mining Data Mining with Rattle Introduction Data Transform Explore A Model Building Framework Unsupervised Modelling Two Class Models Multi Class Models Regression Models Text Mining Evaluation and Deployment Moving into R Troubleshooting R for the Data Miner R Data Graphics in R Understanding Data Preparing Data Building Models Evaluating Models Algorithms Apriori Bagging Bayes Classifier Boosting Cluster Analysis Conditional Trees Hierarchical Clustering K-Means K-Nearest Neighbours Linear Models Logistic Regression Neural Networks Support Vector Machines Text Mining Open Products AlphaMiner Borgelt Data Mining Suite KNime R Rattle Weka Closed Products C4.5 Clementine Equbits Foresight GhostMiner InductionEngine ODM Enterprise Miner Statistica Data Miner TreeNet Virtual Predict Appendicies Glossary Bibliography Index

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Temporal Difference Learning: Learning based on the difference between temporally successive predictions, rather than error between predicted and actual values, so that the learner's current prediction for the current input data more closely matches the next prediction at the next time step, thus learning occurring as predictions change over time. Forms the basis for Reinforcement Learning.

Terminological Logics: Formalise the notion of Frames as structured types, often called Concepts. These logics include a set of syntactic constructs that form concepts, and other, related, notions such as roles, and are based on formal model-theoretic semantics which provide firm definitions for the syntactic constructs of the logic. Synonyms include Description Logic and Concept Language.

Test Set: A portion of a dataset used to test the performance of a model. See also Training Set and Validation Set.

Training Set: A portion of a dataset that is used to build a model. See also Test Set and Validation Set.

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