Swiss-Chocolate: Combining Flipout Regularization and Random Forests with Artificially Built Subsystems to Boost Text-Classification for Sentiment.
Fatih UzdilliMartin JaggiDominic EggerPascal JulmyLeon DerczynskiMark CieliebakPublished in: SemEval@NAACL-HLT (2015)
Keyphrases
- random forests
- text classification
- sentiment analysis
- sentiment classification
- random forest
- stochastic gradient descent
- logistic regression
- decision trees
- ensemble methods
- machine learning algorithms
- text categorization
- naive bayes
- multi label
- tree ensembles
- machine learning
- randomized trees
- text classifiers
- labeled data
- text mining
- regression forests
- decision tree ensembles
- unlabeled data
- feature selection
- k nearest neighbor
- multi class
- accurate classifiers
- prediction accuracy
- head pose estimation
- knn
- active learning
- support vector
- learning algorithm