The phrase "a linear classifier for" is correct and usable in written English.
It can be used in contexts related to machine learning, statistics, or data analysis when discussing a specific type of algorithm or model.
Example: "In this study, we implemented a linear classifier for predicting customer behavior based on their purchase history."
Alternatives: "a linear model for" or "a linear algorithm for".
After feature extraction, we apply a linear classifier for classification.
We used a linear classifier for classification, and the average classification rate for all of the speakers was calculated.
In the experiments, SKI is compared with Chen's method [8], which is a representative block-wise segmentation method and trains a linear classifier for segmentation.
Zheng et al. [16] reported a sensitivity of 95% using a combination of temporal, spatial, and morphological attributes and a linear classifier for 31 subjects, but even in this study the segmentation step was not completely automatic.
This means that a linear classifier for pairs (c, t ) with this kernel decomposes as a set of independent linear classifiers for interactions between molecules and each target protein, which are trained without sharing any information of known ligands between different targets.
A three-stage classification method employing both fixed directional and adaptive filters, in addition to a linear classifier, is introduced for classifying various types of human walking.
A linear classifier is used for the training and testing process.
Two fuzzy classifiers, a Bayesian classifier and a linear classifier, are designed for vocal folds damage detection based on human vowel voices /a:/ and /i:/ only, and the fuzzy classifiers are compared against the Bayesian classifier and linear classifier.
The second step removes these pivot words from a copy of each feature space where then a linear classifier is trained for each pivot using the data from both domains.
It discusses the assumption that all feature vectors from the available classes can be classified correctly using a linear classifier and describes techniques developed for the computation of the corresponding linear functions.
Firstly it is a linear classifier and cannot allow for non linear relationships in the data.
