Results 271 to 280 of about 538,598 (303)
Some of the next articles are maybe not open access.
Related searches:
Related searches:
Nonparametric Discriminant Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence, 1983A nonparametric method of discriminant analysis is proposed. It is based on nonparametric extensions of commonly used scatter matrices. Two advantages result from the use of the proposed nonparametric scatter matrices. First, they are generally of full rank. This provides the ability to specify the number of extracted features desired.
Fukunaga, K., Mantock, J. M.
openaire +3 more sources
Subclass discriminant analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2006Over the years, many Discriminant Analysis (DA) algorithms have been proposed for the study of high-dimensional data in a large variety of problems. Each of these algorithms is tuned to a specific type of data distribution (that which best models the problem at hand).
Manli, Zhu, Aleix M, Martinez
openaire +2 more sources
IEEE Transactions on Neural Networks, 2000
In this article the role of the bootstrap is highlighted for nonlinear discriminant analysis using a feedforward neural network model. Statistical techniques are formulated in terms of the principle of the likelihood of a neural-network model when the data consist of ungrouped binary responses and a set of predictor variables.
M, Tsujitani, T, Koshimizu
openaire +2 more sources
In this article the role of the bootstrap is highlighted for nonlinear discriminant analysis using a feedforward neural network model. Statistical techniques are formulated in terms of the principle of the likelihood of a neural-network model when the data consist of ungrouped binary responses and a set of predictor variables.
M, Tsujitani, T, Koshimizu
openaire +2 more sources
Discriminant Learning Analysis
IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 2008Linear discriminant analysis (LDA) as a dimension reduction method is widely used in classification such as face recognition. However, it suffers from the small sample size (SSS) problem when data dimensionality is greater than the sample size, as in images where features are high dimensional and correlated. In this paper, we propose to address the SSS
Jing, Peng, Peng, Zhang, Norbert, Riedel
openaire +2 more sources
CRC Critical Reviews in Clinical Laboratory Sciences, 1978
Discriminant analysis (DA) is a pattern recognition technique that has been widely applied in medical studies. It allows multivariate observations ("patterns" or points in multidimensional space) to be allocated to previously defined groups (diagnostic categories).
openaire +2 more sources
Discriminant analysis (DA) is a pattern recognition technique that has been widely applied in medical studies. It allows multivariate observations ("patterns" or points in multidimensional space) to be allocated to previously defined groups (diagnostic categories).
openaire +2 more sources
Multi-View Discriminant Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012In many computer vision systems, the same object can be observed at varying viewpoints or even by different sensors, which brings in the challenging demand for recognizing objects from distinct even heterogeneous views. In this work we propose a Multi-view Discriminant Analysis (MvDA) approach, which seeks for a single discriminant common space for ...
Meina, Kan +4 more
openaire +2 more sources
Archives of Pediatrics & Adolescent Medicine, 1984
Discriminant analysis (DA) is a family of statistical methods in which the primary purpose is to predict into which of several discrete groups (eg, diagnostic categories) an individual subject should be classed. This might be called the classification or forecasting function. A second use of DA examines how well the derived discrimination equation fits
openaire +2 more sources
Discriminant analysis (DA) is a family of statistical methods in which the primary purpose is to predict into which of several discrete groups (eg, diagnostic categories) an individual subject should be classed. This might be called the classification or forecasting function. A second use of DA examines how well the derived discrimination equation fits
openaire +2 more sources
WIREs Computational Statistics, 2012
AbstractThe need for classification arises in most scientific pursuits. Typically, there is interest in ‘classifying’ an entity, say, an individual or object, on the basis of some characteristics (feature variables) measured on the entity. This article focuses on the form of classification known as supervised classification or discriminant analysis. It
openaire +3 more sources
AbstractThe need for classification arises in most scientific pursuits. Typically, there is interest in ‘classifying’ an entity, say, an individual or object, on the basis of some characteristics (feature variables) measured on the entity. This article focuses on the form of classification known as supervised classification or discriminant analysis. It
openaire +3 more sources
Sequential Decisional Discriminant Analysis
2007We are describing here a sequential discriminant analysis method which aim is essentially to classify evolutionary data. This method of decision-making is based on the research of principal axes of a configuration of points in the individual-space with a relational inner product.
openaire +3 more sources