'Small training set' diaporamas de présentation

Constrain, Train, Validate and Explain: A Classifier for Mission-Critical Applications

530 views • 42 slides

Support Vector Machines (SVMs) Chapter 5 (Duda et al.)

Support Vector Machines (SVMs) Chapter 5 (Duda et al.). CS479/679 Pattern Recognition Dr. George Bebis. Learning through empirical risk minimization. Estimate g(x) from a finite set of observations by minimizing an error function, for example, the training error or empirical risk :.

560 views • 36 slides

Automatic Measurement of Syntactic Development in Child Language

Automatic Measurement of Syntactic Development in Child Language. Kenji Sagae Language Technologies Institute Student Research Symposium September 2005 Joint work with Alon Lavie and Brian MacWhinney. Using Natural Language Processing in Child Language Research .

493 views • 35 slides

Text classification: In Search of a Representation

Text classification: In Search of a Representation. Stan Matwin School of Information Technology and Engineering University of Ottawa stan@site.uottawa.ca. Outline . Supervised learning=classification ML/DM at U of O Classical approach Attempt at a linguistic representation

417 views • 29 slides

Relevant Previous Algorithms

Relevant Previous Algorithms. Linear discrimination LMS Gradient descent. Features. Simple but powerful Nonlinear functions Multilayers Heuristic. Feedforward Operation Example. y, Perceptron. Fourier’s Theorem. Kolmogorov. Universal expressive power.

295 views • 23 slides

What is the Best Multi-Stage Architecture for Object Recognition

310 views • 21 slides