Sentence examples for many kernels from inspiring English sources

Chinese geneticists are identifying the functions of specific genes and will soon create rice with fewer stalks per plant, but twice as many kernels per stalk, for example, or rice that needs less water or rice with a higher protein content.

It is shown that, in a restrictive case (but consistent to prevent overfitting), the problem boils down to a multiple kernel learning instance with infinitely many kernels.

Many kernels have been published, which cover different aspects of molecular similarity but avoid the loss of information caused by the encoding of the molecule as a descriptor vector.

One kernel program per loop, to map the loop onto the DataFlow hardware; One kernel test program per loop, to test the above; One manager program (no matter how many kernels there are) to move data: (1) Into the DataFlow accelerator,   (2) In between the kernels (if more than one kernel exists), and   (3) Out of the DataFlow accelerator;  .

They are composed of: (a) luminance, (b) gradient intensity, (c) local binary pattern, (d) mean value and (e) standard deviation computed in many kernels of different size, (f) the difference between maximum and minimum values in the kernels, and (g) the pixel value in the median filtered image.

Larvae do not eat the hard kernels, but take bites out of many kernels, lowering the quality of the corn for processing.

There are many kernel functions, such as linear kernel, radial basis function (RBF) kernel, and polynomial kernel, which were commonly used in the SVM.

Many kernel-based learning algorithms have the computational load scaled with the sample size n due to the column size of a full kernel Gram matrix K.

Choosing appropriate values for kernel parameters is one of the key problems in many kernel-based methods because the values of these parameters have significant impact on the performances of these methods.

The deficiency of weak generalization ability for many kernel-based intelligent algorithms such as support vector machines requires large datasets and long training times to ensure the modeling performance, which is computationally expensive and ineffective for online rolling situations.

Moreover, the new kernel is novelly derived from powerful Markov chain theory and can benefit many kernel-based learning methods in general.