Most decision tree classifiers are designed to classify the data with categorical or Boolean class labels.
Most decision tree classifiers are designed to classify the objects whose attributes and class labels are single values.
Furthermore, a multi-class SVM classifier is designed to classify the extracted density character vectors into different density levels.
The proposed classifier is designed to classify AM, BFSK, OFDM, DS-CDMA, 4-ASK, 8-ASK, BPSK, QPSK, 8-PSK, 16-PSK, 16-QAM, and 64-QAM modulation types.
The classifiers were designed to classify the cases into the following 4 groups corresponding to the cervical histology: negative, CIN1, CIN2/3, and cancer (SCC or Adeno-Ca).
The chapter also discusses classifiers that are designed to classify an unknown pattern in the most probable of the classes.
A script independent unified pattern classifier is then designed to classify the numeric postal codes into one of these 25 clusters.
One of the three sublayers in MAC, the convergence sublayer, is designed to classify and map the MSDUs to the designated service class and connection identifier (CID).
Originally, the taxonomy approach was designed to classify knowledge in a hierarchical way.
Text categorization systems are designed to classify documents into a fixed number of predefined categories.
A multilayer perceptron (MLP) was designed to classify non-lampante and lampante virgin olive oils.
