Attention to Detail: The Generalization of Extinction. Tom Barry Promoter: Prof. Dr. Dirk Hermans; Co-Promoter: Dr, Bram Vervliet University of Leuven, Centre for Learning Psychology and Experimental Psychopathology. Background. Methods. Results. Conclusions. Treatment of Simple PhobiaBy nerice
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Generalization. A generalization is a kind of conclusion. Clue Words indicate a generalization. KEY WORDS. NEVER. ALL. NONE. MOST. SOMETIMES. SELDOM. OVERALL. IN GENERAL. EVERYONE. ALWAYS. USUALLY. FEW.
Generalization. TLW: Evaluate and apply generalization to my teaching practice I will: Define generalization and its characteristics through reading and discussion Read and evaluate generalizations, discuss with a partner, and create a plan for application.
A. GENERALIZATION. Generalization – how to decide?. Scale of the map may dictate your needs May seem arbitrary at times, although it helps to simplify for easier viewing sometimes Also, well-defined points can be important Why? . Generalization: Standards.
Glittering Generalization. Weasel words used. Statement jumps from a few cases to all. “Glittering” because it’s falsely attractive Often used by politicians.
A. GENERALIZATION. Generalization – how to decide?. Scale of the map may dictate your needs May seem arbitrary at times, although it helps to simplify for easier viewing sometimes Also, well-defined points can be important Why?. Generalization: Standards.
Model generalization. Brief summary of methods Bias, variance and complexity Brief introduction to Stacking. Brief summary. Capability to learn highly abstract representations. Need expert input to select representations. Shallow classifiers (linear machines). Parametric Nonlinear models.
Hasty Generalization. A hasty generalization is a broad claim based on too-limited evidence. Tristan Shaw. WWW.FRENCH.ABOUT.COM. http://i.ytimg.com/vi/T64H0Gyw2D0/0.jpg. Example 1. Act #1, page 189, speaker: Mrs. Putnam
Map Generalization. Introduction Concepts conventional cartography geographic information systems Developments conceptual models algorithms knowledge representation. Image Processing Division. Introduction. Data presentation display communication Data integration
Model generalization. Test error Bias, variance and complexity In-sample error Cross-validation Bootstrap “Bet on sparsity ”. Model. Training data. Testing data. Model. Testing error rate. Training error rate.