Support Vector Regression (SVR) solves regression problems based on the concept of Support Vector Machine (SVM). In this paper, a new model of SVR with probabilistic constraints is proposed that any of output data and bias are considered the random variables with uniform probability functions. Using the new proposed method, the optimal hyperplane regression can be obtained by solving a quadrati...

It is introduced in the paper the newly developed optimization method the Stochastic Optimization Algorithm with Probability Vector (PSV). It is related to Stochastic Learning Algorithm with Probability Vector for artificial neural networks. Both algorithms are inspired by stochastic iterated function system SIFS for generating the statistically self similar fractals. The PSV is gradient method...

A rapid DNA cloning system is a research interest of many scientists. TA cloning is one of the methods used for the cloning of PCR-amplified DNA molecules. The TA cloning method is a convenient and labor-saving replacement to traditional, restriction enzyme-mediated cloning strategies. A T-vector called pBlueskript ΙΙ SK-1 with the lethal gene ccdB was designed to construct a positive selection...

various statistical methods have been proposed in terms of predicting the outcomes of facing special factors. in the classical approaches,  making the probability distribution or known probability density functions is ordinarily necessary to predict the desired outcome. however, most of the times enough information about the probability distribution of studied variables is not available to the ...

Example 2 (Convexity of a probability vector space) Show that the probability vector space is a convex set. Answer: Consider a random variable X with alphabet X = 1, ..., k. The probability vector space PX = [PX (1) , PX (2) , ..., PX (k)] ∈ R, is the set of all vectors for which PX (i) ≥ 0 ∀i ∈ X , and ∑K i=1 PX (i) = 1. Now consider two probability vectors P (1) X and P (2) X , and the vector

Example 2 (Convexity of a probability vector space) Show that the probability vector space is a convex set. Answer: Consider a random variable X with alphabet X = 1, ..., k. The probability vector space PX = [PX (1) , PX (2) , ..., PX (k)] ∈ R, is the set of all vectors for which PX (i) ≥ 0 ∀i ∈ X , and ∑K i=1 PX (i) = 1. Now consider two probability vectors P (1) X and P (2) X , and the vector

Example 2 (Convexity of a probability vector space) Show that the probability vector space is a convex set. Answer: Consider a random variable X with alphabet X = 1, ..., k. The probability vector space PX = [PX (1) , PX (2) , ..., PX (k)] ∈ R, is the set of all vectors for which PX (i) ≥ 0 ∀i ∈ X , and ∑K i=1 PX (i) = 1. Now consider two probability vectors P (1) X and P (2) X , and the vector