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.@en

Multiscale information provides an opportunity to improve the outcomes of data analysis processes. However, if the multiscale information is not properly summarized in a compact representation, this may lead to problems related to high dimensional data. In addition, in some situations, it is convenient to define dissimilarities directly for the multiscale data obtaining in this way a multiscale dissimilarity representation. When these dissimilarities are specifically designed for the problem, it is even possible that they do not fulfill metric requirements. Therefore, standard statistical analysis techniques may not be easily applicable. We propose a new method to combine non-metric multiscale dissimilarities in a compact representation which is used for classification. The method is based on the extended multiscale dissimilarity space and prototype selection, which allows us to handle the potentially non-metric nature of the dissimilarities and exploit the multiscale information at the same time. This is achieved in such a way that the most informative examples per scale are selected. Experimental results show that the approach is promising since it finds a better trade-off in accuracy and efficiency than its counterpart approaches.@en

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 ten chapters 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.@en

When characterizing teams of people, molecules, or general graphs, it is difficult to encode all information using a single feature vector only. For these objects dissimilarity matrices that do capture the interaction or similarity between the sub-elements (people, atoms, nodes), can be used. This paper compares several representations of dissimilarity matrices, that encode the cluster characteristics, latent dimensionality, or outliers of these matrices. It appears that both the simple eigenvalue spectrum, or histogram of distances are already quite effective, and are able to reach high classification performances in multiple instance learning (MIL) problems. Finally, an analysis on teams of people is given, illustrating the potential use of dissimilarity matrix characterization for business consultancy.@en

In the modern trains operated by the Dutch Railways (Nederlandse Spoorwegen) in the Netherlands, there are on-board train management systems continuously monitoring the conditions of various train modules such as traction, climate, brake electronics and so forth. When an abnormal or particular situation occurs, the system will generate and store an event log on the local disk or on a remote disk using wireless data communications. These diagnostic events might give an indication of the train condition, and currently critical events are selected by business rules to give alarms on failure or malfunction to the control room. To give a better prediction on the trains status based on the condition monitoring data, sequences of diagnostic events instead of individual critical events are analyzed in this work. Moreover, train delays instead of train failures are used as targets for providing more insight on the degeneration behavior of trains. We have adopted the word sequence kernel for learning the similarity between all sequence pairs, where each diagnostic event is considered as a word. To include multi-length word interpretations, we propose to combine the word sequence kernels of various lengths, where length=1 means one word is matched, length=2 means two words are matched, and so on. A kernel machine or similarity-based model can be learned directly on this combined word sequence kernel. The experimental results demonstrate that combining word sequence kernels of different lengths can bring a richer description to similarity measurements and gives better prediction performance.@en