Neural Field Classifiers via Target Encoding and Classification Loss.
Xindi YangZeke XieXiong ZhouBoyu LiuBuhua LiuYi LiuHaoran WangYunfeng CaiMingming SunPublished in: ICLR (2024)
Keyphrases
- support vector
- classification systems
- classification algorithm
- decision trees
- accurate classification
- supervised classification
- classification models
- machine learning algorithms
- pattern recognition
- rule based classifier
- classification method
- training data
- svm classifier
- training samples
- feature selection
- class labels
- classification rate
- classification process
- improves the classification accuracy
- classification accuracy
- multiple classifiers
- majority voting
- neural network
- final classification
- fold cross validation
- discriminant functions
- machine learning methods
- neural classifier
- support vector machine
- training set
- optimum path forest
- multi category
- image classification
- individual classifiers
- feature set
- classifier combination
- binary classifiers
- ensemble classifier
- multiclass classification
- machine learning
- multiple classifier systems
- higher classification accuracy
- feature values
- supervised learning
- network architecture
- fractal image encoding
- probabilistic classifiers
- data stream classification
- imbalanced data sets
- classification procedure
- correctly classified
- roc curve
- multi layer perceptron
- decision boundary
- combining classifiers
- accurate classifiers
- eeg signals
- base classifiers
- ensemble methods
- support vector machine classifiers
- feature subset
- support vector machine svm
- learning rules
- classification decisions
- nearest neighbour
- feature vectors
- sufficient training data
- classifier ensemble