Using the graphical method, we aim to visually overlap and shift sequences to compute their convolution by summing the products of their overlapping values. We present the steps to follow in Table 1: In solving the problem using the graphical method, we first draw the graph of both sequences...
Graphical representation of weight functions Example 6.1 Following data has been taken from a table of property values of a substance, after suppressing information on the units. x↓ y→500600 15 1286 1334.2 25 1285 1333.6 Obtain the value of the function at x = 20, y = 530 using linear ...
CNN’s results indicated that although convolution kernel is effective on images, it does not apply to the URLs dataset in this study. Finally, it should be noted that the Nyström method raised the dimension of the datasets (In our experiments it was raised from 62 to 500). The memory ...
Even though we concentrated on Gaussian blur in this article, many of the discussed principles apply to most convolution filter types. Also, most of the theory applies in case we need a blurred image of reduced size like it is usually needed by the bloom effect, even the linear sampling. T...
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Many proposals have been made to accelerate such primitives, for example using iterative methods such as the conjugate gradient method. In the last decade, these primitives have been accelerated by hardware improvements, notably by graphical processing units (GPUs), fueling massive parallelization. ...
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Stat Comput 28:937–956 Diggle P (1985) A kernel method for smoothing point process data. J Roy Stat Soc: Ser C (Appl Stat) 34(2):138–147 Diggle PJ, Milne RK (1983) Bivariate cox processes: some models for bivariate spatial point patterns. J Roy Stat Soc: Ser B (Methodol) 45(...
The proposed method achieves an overall F-score of 67% which outperforms the best performing system by 1.9%. We believe that the strength of our method comes from using a diverse set of features. In addition, the one-against-one strategy [23] used in the DDI type classification contributes...
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