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PatReco: Introduction

PatReco: Introduction. Alexandros Potamianos Dept of ECE, Tech. Univ. of Crete Fall 2004-2005. PatReco:Applications. Speech/audio/music/sounds Speech recognition, Speaker verification/id, Image/video OCR, AVASR, Face id, Fingerpring id, Video segmentation Text/Language

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PatReco: Introduction

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  1. PatReco: Introduction Alexandros Potamianos Dept of ECE, Tech. Univ. of Crete Fall 2004-2005

  2. PatReco:Applications • Speech/audio/music/sounds • Speech recognition, Speaker verification/id, • Image/video • OCR, AVASR, Face id, Fingerpring id, Video segmentation • Text/Language • Machine translatoin, document class., lnag mod., text underst. • Medical/Biology • Disease diagnosis, DNA sequencing, Gene disease models • Other Data • User modeling (books/music), Ling analysis (web), Games

  3. Basic Concepts • Why statistical modeling? • Variability: differences between two examples of the same class in training • Mismatch: differences between two examples of the same class (one in training one in testing) • Learning modes: • Supervised learning: class labels known • Unsupervised learning: class labels unknown • Re-inforced learning: only positive/negative feedback

  4. Basic Concepts • Feature selection • Separate classes, Low correlation • Model selection • Model type, Model order • Prior knowledge • E.g., a priori class probability • Missing features/observations • Modeling of time series • Correlation in time (model?), segmentation

  5. PatReco: Algorithms • Parametric vs Non-Parametric • Supervised vs Unsupervised • Basic Algorithms: • Bayesian • Non-parametric • Discriminant Functions • Non-Metric Methods

  6. PatReco: Algorithms • Bayesian methods • Formulation (describe class characteristics) • Bayes classifier • Maximum likelihood estimation • Bayesian learning • Estimation-Maximization • Markov models, hidden Markov models • Bayesian Nets • Non-parametric • Parzen windows • Nearest Neighbour

  7. PatReco: Algorithms • Discriminant Functions • Formulation (describe boundary) • Learning: Gradient descent • Perceptron • MSE=minimum squared error • LMS=least mean squares • Neural Net generalizations • Support vector machines • Non-Metric Methods • Classification and Regression Trees • String Matching

  8. PatReco: Algorithms • Unsupervised Learning: • Mixture of Gaussians • K-means • Other not-covered • Multi-layered Neural Nets • Stochastic Learning (Simulated Annealing) • Genetic Algorithms • Fuzzy Algorithms • Etc…

  9. PatReco: Problem Solving • Data Collection • Data Analysis • Feature Selection • Model Selection • Model Training • Classification • Classifier Evaluation

  10. PatReco: Problem Solving • Data Collection • Data Analysis • Feature Selection • Model Selection • Model Training • Classification • Classifier Evaluation

  11. PatReco: Problem Solving • Data Collection • Data Analysis • Feature Selection • Model Selection • Model Training • Classification • Classifier Evaluation

  12. PatReco: Problem Solving • Data Collection • Data Analysis • Feature Selection • Model Selection • Model Training • Classification • Classifier Evaluation

  13. Evaluation • Training Data Set • 1234 examples of class 1 and class 2 • Testing/Evaluation Data Set • 134 examples of class 1 and class 2 • Misclassification Error Rate • Training: 11.61% (150 errors) • Testing: 13.43% (18 errors) • Correct for chance (Training 22%, Testing 26%) • Why?

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