This paper studies the addition of linear constraints to Support Vector Regression when kernel is linear. Adding those into problem allows add prior knowledge on estimator obtained, such as finding positive vector, probability vector or monotone data. We prove that related optimization stays a semi-definite quadratic problem. also propose generalization Sequential Minimal Optimization algorithm...

let $g$ be a finite group. we construct the prime graph of $ g $,which is denoted by $ gamma(g) $ as follows: the vertex set of thisgraph is the prime divisors of $ |g| $ and two distinct vertices $ p$ and $ q $ are joined by an edge if and only if $ g $ contains anelement of order $ pq $.in this paper, we determine finite groups $ g $ with $ gamma(g) =gamma(l_3(q)) $, $2 leq q < 100 $ and prov...