Critique [of Institutional Racism]

“The complex of concepts which western peoples use to process data and make decisions are the ultimate enemy of minorities. . . .” As an educator, and especially as one involved in educating journalists, I found myself drawn to Deloria’s statement

[Review of] Gretchen M. Bataille and Charles L. P. Silet (Eds.), The Pretend Indians: Images of Native Americans in the Movies

Those of us concerned with mass media stereotyping are especially grateful for this well-edited reader, but all persons interested in Native Americans and their ”popular“ images will find it enjoyable and useful

The integration of system specifications and program coding

Experience in maintaining up-to-date documentation for one module of the large-scale Medical Literature Analysis and Retrieval System 2 (MEDLARS 2) is described. Several innovative techniques were explored in the development of this system's data management environment, particularly those that use PL/I as an automatic documenter. The PL/I data description section can provide automatic documentation by means of a master description of data elements that has long and highly meaningful mnemonic names and a formalized technique for the production of descriptive commentary. The techniques discussed are practical methods that employ the computer during system development in a manner that assists system implementation, provides interim documentation for customer review, and satisfies some of the deliverable documentation requirements

Response Surface Methodology for Optimizing Hyper Parameters

The performance of an algorithm often largely depends on some hyper parameter which should be optimized before its usage. Since most conventional optimization methods suffer from some drawbacks, we developed an alternative way to find the best hyper parameter values. Contrary to the well known procedures, the new optimization algorithm is based on statistical methods since it uses a combination of Linear Mixed Effect Models and Response Surface Methodology techniques. In particular, the Method of Steepest Ascent which is well known for the case of an Ordinary Least Squares setting and a linear response surface has been generalized to be applicable for repeated measurements situations and for response surfaces of order o ?Ü 2. --repeated measurements,Random Intercepts Model,deterministic error terms,Method of Steepest Ascent,Support Vector Machine

Latent Factor Prediction Pursuit for Rank Deficient Regressors

In simulation studies Latent Factor Prediction Pursuit outperformed classical reduced rank regression methods. The algorithm described so far for Latent Factor Prediction Pursuit had two shortcomings: It was only implemented for situations where the explanatory variables were of full colum rank. Also instead of the projection matrix only the regression matrix was calculated. These problems are addressed by a new algorithm which finds the prediction optimal projection. --simulated annealing,prediction oriented projections,reduced rank regression,rank deficient regressors,simulation study

A Note on the Dimension of the Projection Space in a Latent Factor Regression Model with Application to Business Cycle Classification

In this paper it is shown that the number of latent factors in a multiple multivariate regression model need not be larger than the number of the response variables in order to achieve an optimal prediction. The practical importance of this lemma is outlined and an application of such a projection on latent factors in a classification example is given. --Latent Factor Models,Projection Matrix,Regression,Classification

Prediction Optimal Classification of Business Phases

Linear Discriminant Analysis (LDA) performs well for classifica- tion of business phases – even though the premises of an LDA are not met. As the variables are highly correlated there are numerical as well as interpretational shortcomings. By transforming the classification problem to a regression setting both problems can be addressed by a computer-intensive prediction oriented method which also improves the classification performance. --

Localized Linear Discriminant Analysis

Despite its age, the Linear Discriminant Analysis performs well even in situations where the underlying premises like normally distributed data with constant covariance matrices over all classes are not met. It is, however, a global technique that does not regard the nature of an individual observation to be classified. By weighting each training observation according to its distance to the observation of interest, a global classifier can be transformed into an observation specific approach. So far, this has been done for logistic discrimination. By using LDA instead, the computation of the local classifier is much simpler. Moreover, it is ready for applications in multi-class situations. --classification,local models,LDA

Different Subspace Classification

We introduce the idea of Characteristic Regions to solve a classification problem. By identifying regions in which classes are dense (i.e. many observations) and also relevant (for discrimination) we can characterize the different classes. These Characteristic Regions are used to generate a classification rule. The result can be visualized so the user is provided with an insight into data for an easy interpretation. --

Improving Feature Extraction by Replacing the Fisher Criterion by an Upper Error Bound

A lot of alternatives and constraints have been proposed in order to improve the Fisher criterion. But most of them are not linked to the error rate, the primary interest in many applications of classification. By introducing an upper bound for the error rate a criterion is developed which can improve the classification performance. --Fisher criterion,Linear discriminant analysis,Feature extraction