Recent applications and developments based on support vector machines (SVMs) have shown that using multiple kernels instead of a single one can enhance classifier performance. However, there are few reports on performance of the kernel‐based Fisher discriminant analysis (kernel‐based FDA) method with multiple kernels. This paper proposes a multiple kernel construction ...

chickpea (cicer arietinum l.) is a native crop of asia which is grown worldwide including iran.in this study,different selection criteria including correlation,regression and path analysis were used to improve yield.the experimental design was split plot with rcbd replicated four times in which three sowing dates (5 january,4 february,and 5 march) were used in main plots and four genotypes;two ...

This study examines the relationships between atmospheric influences and grain yields as well as yield components: crop density (ears m−2), number of kernels per ear and kernel weight of winter rye. The study uses data of a long-term field experiment at Berlin-Dahlem for the period between 1962 and 1996. The crop density and kernel number of winter rye were positively influenced by warm and sun...

in order to evaluate some morphological traits of durum wheat genotypes, 18 elite lines selected from advanced regional durum wheat yield trials. these materials along, with two checks dena (durum wheat) and parsi (bread wheat) were examined using rcb design with three replications at isfahan research station in two consecutive cropping season. during the cropping season some trials such as the...

Knowledge of maize (Zea mays L.) kernel weight (KW) and number (KN) response to nitrogen (N) fertilization can help understand the physiological basis behind overall grain yield N fertilizer, but such data are rare. To fill this gap, we studied how KN KW respond fertilization. We first analyzed its components in 23 field experiments (site × year combinations) from Argentina United States. Secon...

Kernel combination is meant to improve the performance of single kernels and avoid the difficulty of kernel selection. The most common way of combining kernels is to compute their weighted sum. Usually, the kernels are assumed to exist in independent empirical feature spaces and therefore were combined without considering their relationships. To take these relationships into consideration in ke...