Machine Learning Test: Vapnik-Chervonenkis Dimension and Model Complexity
In this content, you will explore the Vapnik-Chervonenkis dimension, its importance in estimating model complexity, and its applications in machine learning verification tests. The content covers topics such as estimating the number of patterns, free parameters, and classification probability. References to essential materials and resources for further study are also included.
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Presentation Transcript
? Machine Learning (Part II) Test Angelo Ciaramella
Question 19 Model complessity Question The Vapnik-Chervonenkis dimension can be used for Estimate the number of patterns Estimate the number a free parameters Estimate the probability of classification ML Verification tests
Vapnik-Chervonenkis dimension Goal Worst-case performance for a particular trained network Theorem ML Verification tests 3
Vapnik-Chervonenkis dimension ML Verification tests 4
Vapnik-Chervonenkis dimension NN M units, W weights ML Verification tests Two layers and threshold units d inputs For large networks 5
References Material Slides Video Lessons Books J. C. Bishop, Neural Networks for Pattern Recognition, Oxford University Press, 1995 J. C. Bishop, Pattern Recognition and Machine Learning, Springer, 2006 ML Verification tests
