Sentence examples for bits to each feature from inspiring English sources

Apart from the one-dimensional quantizer design, some papers focus on assigning a varying number of quantization bits to each feature.

So far, several bit allocation principles have been proposed: fixed bit allocation (FBA) [10, 11, 17] simply assigns a fixed number of bits to each feature.

These schemes base upon either a fixed-bit allocation principle (assigning a fixed number of bits to each feature dimension) [4 7, 10, 13, 16, 20] or a dynamic-bit allocation principle (assigning a different number of bits to each feature dimension) [1, 3, 17 19, 21].

On the contrary, the detection rate optimized bit allocation (DROBA) [19] and the area under the FRR curve optimized bit allocation (AUF-OBA) [20], assign a variable number of bits to each feature, according to the features' distinctiveness.

Teoh et al. [18, 19] developed a bit-allocation approach based on an unsupervised equal-width quantization with a BRGC-encoding scheme to compose a long binary string per user by assigning different number of bits to each feature dimension according to the SD of each estimated user pdf.

6. e-GA-MTL also uses a genetic algorithm to search features, and employs two bits to represent one feature; some features are considered as irrelevant and discarded, some of the selected features serve as input to the ANN, and some of the features are added to the output.

When substantial quantization intervals are constructed or a large number of bits are allocated to each feature dimension, equal width (EW) quantization offers an approximate continuous-to-discrete mapping.

By and large, our general findings can be summarized in the following three aspects: When substantial quantization intervals are constructed or a large number of bits are allocated to each feature dimension, equal width (EW) quantization offers an approximate continuous-to-discrete mapping.

In this paper, we present a detection rate optimized bit allocation (DROBA) principle, which assigns more bits to discriminative features and fewer bits to nondiscriminative features.

In this paper, we deal with quantizer design rather than assigning the quantization bits to features.

It difiers from GA-MTL in its binary chromosome; instead of only one bit, two bits are used to represent each feature, where 00 means the corresponding feature is discarded, 10 means it is used as input, 01 means it is added to the output, and 11 means it is used as input and added to the output simultaneously.