This paper presents a Devnagari Numerical recognition method based on statistical discriminant functions. 17 geometric features based on pixel connectivity, lines, line directions, holes, image area, perimeter, eccentricity, solidity, orientation etc. are used for representing the numerals. Five discriminant functions viz. Linear, Quadratic, Diaglinear, Diagquadratic and Mahalanobis distance ar...

We present local discriminative Gaussian (LDG) dimensionality reduction, a supervised dimensionality reduction technique for classification. The LDG objective function is an approximation to the leave-one-out training error of a local quadratic discriminant analysis classifier, and thus acts locally to each training point in order to find a mapping where similar data can be discriminated from d...

We present algorithms for constructing and tabulating degree-` dihedral extensions of Fq(x), where q ≡ 1 mod 2`. We begin with a Kummertheoretic algorithm for constructing these function fields with prescribed ramification and fixed quadratic resolvent field. This algorithm is based on the proof of our main theorem, which gives an exact count for such fields. We then use this construction metho...

We propose a penalized likelihood method to jointly estimate multiple precision matrices for use in quadratic discriminant analysis and model based clustering. A ridge penalty and a ridge fusion penalty are used to introduce shrinkage and promote similarity between precision matrix estimates. Block-wise coordinate descent is used for optimization, and validation likelihood is used for tuning pa...

This paper discusses the large-deviation principle of discriminant statistics for Gaussian locally stationary processes. First, large-deviation theorems for quadratic forms and the log-likelihood ratio for a Gaussian locally stationary process with a mean function are proved. Their asymptotics are described by the large deviation rate functions. Second, we consider the situations where processe...

In this paper, the performance of different generative methods for the classification of cervical nuclei are compared in order to detect cancer of cervix. These methods include classical Bayesian approaches, such as Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA) or Mixture Discriminant Analysis (MDA) and a high-dimensional approach (HDDA) recently developed. The class...

We propose new quantum algorithms to solve the regulator and the principal ideal problem in a real-quadratic number field. We improve the algorithms proposed by Hallgren ([Hal02b], [Hal07]) by using two different techniques. The first improvement is the usage of a period function which is not one-to-one on its period. We show that even in this case Shor’s algorithm computes the period with cons...

This article describes an application of three well{known statistical methods in the eld of game{tree search: using a large number of classi ed Othello positions, feature weights for evaluation functions with a game{phase{independent meaning are estimated by means of logistic regression, Fisher's linear discriminant, and the quadratic discriminant function for normally distributed features. The...

MOTIVATION In order to design effective HIV inhibitors, studying and understanding the mechanism of HIV protease cleavage specification is critical. Various methods have been developed to explore the specificity of HIV protease cleavage activity. However, success in both extracting discriminant rules and maintaining high prediction accuracy is still challenging. The earlier study had employed g...

to assess the genetic diversity for morphological and agronomic traits in iranian oriental tobacco germplasm,100 oriental and semi-oriental tobacco genotypes from the urmieh tobacco research center collection were evaluated in the field using a simple square lattice design with two replications. some agronomic traits were measured on five randomly selected plants in each plot. genotypes were cl...