Selecting parental materials for hybridization by discriminant function

Selecting parental materials for hybridization by measuring genetic diversity

Selecting discriminant eigenfaces for face recognition

In realistic face recognition applications, such as surveillance photo identification, supervised learning algorithms usually fail when only one training sample per subject is available. The lack of training samples and the considerable image variations due to aging, illumination and pose variations, make recognition a challenging task. This letter proposes a development of the traditional eige...

Selecting Materials for Radiation Balanced Lasers

Well-characterized solid-state laser materials are evaluated for performance in radiation balanced laser systems. New figures-of-merit are developed and applied to ytterbium doped materials. Superior performance is predicted for high cross section tungstate materials. Photothermal deflection experiments on samples of Yb doped KGd(WO4)2 confirm anti-Stokes fluorescence cooling. Observations of o...

Prediction of Protein Function by Discriminant Analysis*

Approximately 53% of the protein sequences in the National Biomedical Research Foundation (NBRF) database can be allocated to one of 26 functional classes, each of which can be characterized by the joint occurrence of four or fewer attributes. The attributes reflect collective physicochemical properties of the sequences in a class, ranging from simple characteristics of composition, such as ave...

Discriminant Function Analysis (da)

Introduction Discriminant function analysis is used to determine which continuous variables discriminate between two or more naturally occurring groups. For example, a researcher may want to investigate which variables discriminate between fruits eaten by (1) primates, (2) birds, or (3) squirrels. For that purpose, the researcher could collect data on numerous fruit characteristics of those spe...

A simple discriminant function for hepatic disease.

Tests of hepatic function do not correlate highly and this, together with their skew distribution, makes the information provided by them difficult to interpret. By means of an appropriate transformation it is possible to normalise their distributions so that the information from them can be combined in the form of a discriminant function which is not only easy to interpret but gives an index o...