Introduction |
- Preprocessing
- e.g.: segmentation, windowing, FFT, MFCC (mel-frequency cepstrum coefficients)
- Feature extraction
- e.g.: centroid, area, mean
- Feature selection
- Classification
- Training: ground-truth
- Validation
- holdout method
- k-fold cross-validation, leave-one-out
- Bootstrapping (resampling with replacement) (bagging)
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Classifiers (supervised) |
- Bayes classifier (ppt)
- Support Vector Machines
- Boosting algorithms
- Hidden Markov models
- Non-parametric density estimation (distribution-free)
- k-nearest neighbour (non-greedy, lazy)
- Neural networks (greedy)
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Clustering (unsupervised) |
- Hierachical methods
- k-means (demo)
- Self Organizing Maps (demo)
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Resources
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