Datasets and algorithms for incremental/on-line learning
Stationary datasets:
- [Border] (https://github.com/vlosing/Online-learning/tree/master/datasets/border)
- [Overlap] (https://github.com/vlosing/Online-learning/tree/master/datasets/overlap)
- [Letter] (https://archive.ics.uci.edu/ml/datasets/Letter+Recognition)
- [Outdoor] (https://github.com/vlosing/Online-learning/tree/master/datasets/outdoor)
- [COIL] (https://github.com/vlosing/Online-learning/tree/master/datasets/COIL)
- [DNA] (https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/multiclass.html#dna)
- [USPS] (https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/multiclass.html#usps)
- [Isolet] (https://archive.ics.uci.edu/ml/datasets/ISOLET)
- [MNist] (https://github.com/vlosing/Online-learning/tree/master/datasets/MNIST)
- [Gisette] (https://archive.ics.uci.edu/ml/datasets/Gisette)
- [Satimage] (https://archive.ics.uci.edu/ml/machine-learning-databases/statlog/satimage/)
- [Poker] (https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/multiclass.html#poker)
- [Pendigits] (https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/multiclass.html)
- [Sector] (https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/multiclass.html)
Algorithms:
- [Incremental Support Vector Machine (ISVM)] (https://github.com/diehl/Incremental-SVM-Learning-in-MATLAB)
- [On-line Random Forest (ORF)] (https://github.com/amirsaffari/online-multiclass-lpboost)
- [Incremental Learning Vector Quantization (ILVQ)] TODO
- [Learn++ (LPP)] (https://github.com/gditzler/IncrementalLearning)
- [Learn++.NSE (LPPNSE)] (https://github.com/gditzler/IncrementalLearning)
- [Incremental Extreme Learning Machine (IELM)] (http://www3.ntu.edu.sg/home/egbhuang/elm_codes.html)
- [Stochastic Gradient Descent (SGD)] (http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.SGDClassifier.html)
- [Gaussian Naive Bayes (GNB)] (http://scikit-learn.org/stable/modules/generated/sklearn.naive_bayes.GaussianNB.html#sklearn.naive_bayes.GaussianNB)