On the accuracy of statistical pattern recognizers

Applied Science

Pattern Recognition as a Human Centered non-Euclidean Problem

Regularities in the world are human defined. Patterns in the observed phenomena are there because we define and recognize them as such. Automatic pattern recognition tries to bridge the gap between human judgment and measurements made by artificial sensors. This is done in two steps: representation and generalization. Traditional representations of real world objects to be recognized, like features and pixels, either neglect possibly significant aspects of the objects, or neglect their dependencies. We therefor reconsider human recognition and observe that it is based on our direct experience of similarity or dissimilarity of objects. Using these concepts, a pattern recognition system can be defined in a natural way by a pairwise comparison of objects. This results in the dissimilarity representation for pattern recognition. An analysis of dissimilarity measures optimized for performance shows that they tend to be non-Euclidean. The Euclidean vector spaces, traditionally used in pattern recognition and machine learning may thereby be suboptimal. The causes and consequences of the use of non-Euclidean representations will be discussed. It is conjectured that human judgment of object differences result in these non-Euclidean representations as object structure is taken into account.MediamaticsElectrical Engineering, Mathematics and Computer Scienc

The Origin of Patterns

The question is discussed from where the patterns arise that are recognized in the world. Are they elements of the outside world, or do they originate from the concepts that live in the mind of the observer? It is argued that they are created during observation, due to the knowledge on which the observation ability is based. For an experienced observer this may result in a direct recognition of an object or phenomenon without any reasoning. Afterwards and using conscious effort he may be able to supply features or arguments that he might have used for his recognition. The discussion is phrased in the philosophical debate between monism, in which the observer is an element of the observed world, and dualism, in which these two are fully separated. Direct recognition can be understood from a monistic point of view. After the definition of features and the formulation of a reasoning, dualism may arise. An artificial pattern recognition system based on these specifications thereby creates a clear dualistic situation. It fully separates the two worlds by physical sensors and mechanical reasoning. This dualistic position can be solved by a responsible integration of artificially intelligent systems in human controlled applications. A set of simple experiments based on the classification of histopathological slides is presented to illustrate the discussion.Pattern Recognition and Bioinformatic

Pattern Recognition: Introduction and Terminology

This ebook gives the starting student an introduction into the eld of pattern recognition. It may serve as reference to others by giving intuitive descriptions of the terminology. The book is the rst in a series of ebooks on topics and examples in the eld. Our goal is an informal explanation of the concepts. For thorough mathematical descriptions we refer to the textbooks and lectures. In tenchapters the topics of pattern recognition are summarized and its terminology is introduced. In the glossary about 200 terms are described. All glossary terms are linked, forward and backward by hypertext. In the glossary chapter external links are provided to internet pages, papers tutorials, Wikipedia entries, examples, etcetera. Internal links are in dark blue in order to preserve the readability. External links are in blue. This ebook is offered by the authors of a website on pattern recognition tools, http://37steps.com/. Here more information, software, data and examples can be found. The book itself does not assume the use of specific software. The code for generating the examples, however, is written in Matlab using PRTools. It can be inspected by clicking on the gures or example links.e-bookPattern Recognition and Bioinformatic

Statistische patroonherkenning

Delft University of Technolog

Efficient Multiclass ROC Approximation by Decomposition via Confusion Matrix Perturbation Analysis

Receiver operator characteristic (ROC) analysis has become a standard tool in the design and evaluation of two-class classification problems. It allows for an analysis that incorporates all possible priors, costs, and operating points, which is important in many real problems, where conditions are often nonideal. Extending this to the multiclass case is attractive, conferring the benefits of ROC analysis to a multitude of new problems. Even though theROCanalysis extends theoretically to the multiclass case, the exponential computational complexity as a function of the number of classes is restrictive. In this paper, we show that the multiclass ROC can often be simplified considerably because some ROC dimensions are independent of each other. We present an algorithm that analyzes interactions between various ROC dimensions, identifying independent classes, and groups of interacting classes, allowing the ROC to be decomposed. The resulting decomposed ROC hypersurface can be interrogated in a similar fashion to the ideal case, allowing for approaches such as cost sensitive and Neyman-Pearson optimization, as well as the volume under the ROC. An extensive bouquet of examples and experiments demonstrates the potential of this methodology.MediamaticsElectrical Engineering, Mathematics and Computer Scienc

Estimating the Reliability of Neural Network Classifications

Applied Science

Fluorescence Ratio Imaging

Applied Science