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

Sometimesnoveloroutlierdatahastobedetected.Theoutliersmayindicatesomeinterestingrareevent,ortheyshouldbedisregardedbecausetheycannotbereliablyprocessedfurther.Intheidealcasethattheobjectsarerepresentedbyverygoodfeatures,thegenuinedataformsacompactclusterandagoodoutliermeasureisthedistancetotheclustercenter.Thispaperproposesthreenewformulationsto¿ndagoodclustercentertogetherwithanoptimizedp-distancemeasure.Experimentsshowthatforsomerealworlddatasetsverygoodclassi¿cationresultsareobtainedandthat,morespeci¿cally,the1-distanceisparticularlysuitedfordatasetscontainingdiscretefeaturevalues.@en

Pairwiseproximitiesdescribethepropertiesofobjectsintermsoftheirsimilarities.Byusingdi¿erentdistance-basedfunctionsonemayencodedi¿erentcharacteristicsofagivenproblem.However,tousetheframeworkofstatisticalpatternrecognitionsomevectorrepresentationshouldbeconstructed.Oneofthesimplestwaystodothatistode¿neanisometricembeddingtosomevectorspace.Inthiswork,wewillfocusonalinearembeddingintoa(pseudo-)Euclideanspace. Thisisusuallywellde¿nedfortrainingdata.Someinadequacy,however,appearswhenprojectingnewortestobjectsduetotheresultingprojectionerrors.Inthispaperweproposeanaugmentedembeddingalgorithmthatenlargesthedimensionalityofthespacesuchthattheresultingprojectionerrorvanishes.Ourpreliminaryresultsshowthatitmayleadtoabetterclassi¿cationaccuracy,especiallyfordatawithhighintrinsicdimensionality.@en

StatisticallearningalgorithmsoftenrelyontheEuclideandistance.Inpractice,non-Euclideanornon-metricdissimilaritymeasuresmayarisewhencontours,spectraorshapesarecomparedbyeditdistancesorasaconsequenceofrobustobjectmatching[1,2].Itisanopenissuewhethersuchmeasuresareadvantageousforstatisticallearningorwhethertheyshouldbeconstrainedtoobeythemetricaxioms. Thek-nearestneighbor(NN)ruleiswidelyappliedtogeneraldissimilaritydataasthemostnaturalapproach.Alternativemethodsexistthatembedsuchdataintosuitablerepresentationspacesinwhichstatisticalclassi¿ersareconstructed[3].Inthispaper,weinvestigatetherelationbetweennon-Euclideanaspectsofdissimilaritydataandtheclassi¿cationperformanceofthedirectNNruleandsomeclassi¿erstrainedinrepresentationspaces.Thisisevaluatedonaparameterizedfamilyofeditdistances,inwhichparametervaluescontrolthestrengthofnon-Euclideanbehavior.Our¿ndingisthatthediscriminativepowerofthismeasureincreaseswithincreasingnon-Euclideanandnon-metricaspectsuntilacertainoptimumisreached.Theconclusionisthatstatisticalclassi¿ersperformwellandtheoptimalvaluesoftheparameterscharacterizeanon-Euclideanandsomewhatnon-metricmeasure@en

Prototype-based classification relies on the distances between the examples to be classified and carefully chosen prototypes. A small set of prototypes is of interest to keep the computational complexity low, while maintaining high classification accuracy. An experimental study of some old and new prototype optimisation techniques is presented, in which the prototypes are either selected or generated from the given data. These condensing techniques are evaluated on real data, represented in vector spaces, by comparing their resulting reduction rates and classification performance. Usually the determination of prototypes is studied in relation with the nearest neighbour rule. We will show that the use of more general dissimilarity-based classifiers can be more beneficial. An important point in our study is that the adaptive condensing schemes here discussed allow the user to choose the number of prototypes freely according to the needs. If such techniques are combined with linear dissimilarity-based classifiers, they provide the best trade-off of small condensed sets and high classification accuracy. Keywords: Dissimilarity representation; Prototype selection; Adaptive condensing; EM algorithm; Normal density based classifier; Nearest neighbour rule@en