Mixed usage of MATLAB and visual C for improving classification time and training time of FCM classifier.
Takuya KobayashiHidetomo IchihashiKatsuhiro HondaAkira NotsuPublished in: SCIS&ISIS (2012)
Keyphrases
- training set
- training samples
- training process
- classification method
- classification scheme
- classification process
- classification algorithm
- supervised learning
- training examples
- training phase
- classification performances
- svm classifier
- final classification
- k nearest neighbour
- class labels
- multi layer perceptron
- support vector machine
- learning vector quantization
- support vector
- classification rate
- decision trees
- feature space
- sufficient training data
- avoid overfitting
- nearest neighbor classifier
- feature selection
- classification accuracy
- classifier training
- higher classification accuracy
- text classifiers
- machine learning
- training data
- image classification
- discriminative classifiers
- support vector machine svm
- classification models
- knn
- training dataset
- decision boundary
- naive bayes classifier
- boosted classifiers
- bayesian classifier
- feature set
- multi category
- multiple classifiers
- individual classifiers
- discriminative learning
- feature selection and classifier
- probabilistic classifiers
- fold cross validation
- neural network
- text classification
- text categorization
- pattern classification
- learning algorithm
- mercer kernel
- positive training examples
- classifier combination
- supervised training
- feature vectors
- k means
- associative classifiers
- multi class
- multiclass classification
- ensemble classifier
- binary classifiers
- naive bayes
- fuzzy c means
- roc curve