Sentence examples for models for classifications from inspiring English sources

It was not successful to build robust PLS-DA models for classifications in which data from different VOIs were combined.

The proposed technique relies on statistical features and employs Hidden Markov Models for classification.

In present paper models for classification of carcinogenic compounds using MDL and Dragon descriptors were developed.

ClinProTools generates pattern recognition models for classification and prediction and allows graphical visualization of the processed spectral profiles and generation of statistical classification models.

To solve this problem, the well-known Simpson's min max models for classification (min max classification) were applied by Panella [24, 25] and Dung [20].

A mixture of experts refers to the problem of using and combining many experts (e.g., classification or regression models) for classification or prediction purpose [12].

Therefore, the contribution of each feature is tuned according to its relevance for classification which leads to more generalizable and interpretable sparse models for classification.

Because we would like to recognize four emotional speech styles and two basic types of gender, we need to obtain four trained emotion models for classification of speech pronounced by male speakers and four models for classification of sentences spoken by female speakers, and two summary models for gender recognition (trained on the data of sentences pronounced in all classified emotional styles).

The clustering in input output space mentioned in the previous works combines a linear cluster (e.g. hyperplane cluster) and Simpson's min max models for classification (min max classification).

To make the system robust to various types of sound sources, we designed a tree of reference sound models for classification, in which especially generated total three of GMMs for external sounds according to different characteristics of frequency distributions.

The most commonly used performance measures including overall accuracy, kappa index, receiver operating characteristics (ROC), and area under the ROC curve (AUC) were employed to evaluate the accuracy of the models for classification.