Sentence examples for small feature dimension from inspiring English sources

It is observed that the proposed feature extraction method consistently offers better classification accuracy compared to various available methods reported in this paper despite having a very small feature dimension.

The proposed algorithms, especially mean and standard deviation feature vector, have small feature dimensions.

Mean and standard deviation feature vector also provides similar performance and has less complexity due to its smaller feature dimension.

Fundamentally, the proposed I-BGLAM feature extractor which focuses on the gray level of the wood images is rotational invariant and has smaller feature dimension since only discriminative features are considered.

In case of Subject 4, it is found that the average classification accuracy obtained for the proposed method is very close to PAR6 Po) despite having a very smaller feature dimension.

In this paper, we use the 3D joint histogram of these three operators to generate textural features of breast cancer biopsy images, and the joint combination of the three components gives better classification than when using conventional LBPs and provides a smaller feature dimension.

To lower the risk of overfitting, the basic linear kernel is used in the SVM to keep the least model complexity for situations when sample size is small but feature dimension is high.

Since the sample size is far smaller than the feature dimension in a typical HTS gene expression experiment, the conventional training-test data partition of 70 30 % (also known as holdout validation) is not very appropriate for the evaluation of gene selection approaches.

When the distribution of the data is complex and/or the training set is small compared to the feature dimension, the combined decision of an ensemble of multiple classifiers can be used to improve the performance of a single classification rule [ 13].

However, the results of RBF would be slightly unsatisfactory when dealing with small sample which has higher feature dimension and fewer numbers.

The experimental results indicated that the depth of the formed parts increased with an increase in the grain size; the forming depth decreased significantly when the feature dimension was smaller than a critical value.