DATA MINING Desktop Survival Guide by Graham Williams
Desktop Survival Project Home Rattle Introduction Getting Started Interacting with Rattle Data Exploring Data Test Transforming Data Cluster Analysis Data Graphics in R Understanding Data Preparing Data Descriptive and Predictive Analytics Issues Evaluating Models Reporting Cluster Analysis Text Mining Text Mining Algorithms Bagging Bayes Classifier Cluster Analysis Conditional Trees Hierarchical Clustering K-Nearest Neighbours Linear Models Neural Networks Support Vector Machines 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 Installing Rattle 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 Refer[reinforcement-learning]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 Refer[traininset]Training Set and Refer[validationset]Validation Set.

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

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