Boosting, Voting Classifiers and Randomized Sample Compression Schemes.
Arthur da CunhaKasper Green LarsenMartin RitzertPublished in: CoRR (2024)
Keyphrases
- compression scheme
- majority voting
- weighted voting
- image compression
- ensemble classifier
- ensemble learning
- weak classifiers
- randomized trees
- compression ratio
- feature selection
- decision stumps
- ensemble methods
- data compression
- weak learners
- boosting algorithms
- compression algorithm
- decision forest
- boosting framework
- base classifiers
- support vector
- run length encoding
- classifier ensemble
- multiple classifier systems
- entropy coding
- fusion methods
- quantization scheme
- training set
- adaboost algorithm
- training data
- decision trees
- learning algorithm
- sample size
- image quality
- multiscale
- uniform quantization
- boosted classifiers
- cost sensitive
- naive bayes
- training samples
- feature set
- support vector machine
- bitmap indices
- machine learning