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E.M. Pekalska

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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 ...

The dissimilarity representation for structural pattern recognition

Sperical embeddings for non-euclidean dissimilarities

Non-euclidean dissimilarities: causes and informativeness

Feature-based dissimilarity space classification

Clustering-based construction of hidden Markov models for generative kernels

Minimum spanning tree based one-class classifier

Component-based discriminative classification for hidden Markov models

On euclidean correction for non-euclidean dissimilarities

Beyond traditional kernels:classification in two dissimilarity-based representation spaces

On refining dissimilarity matrices for an improved NN learning

The science of pattern recognition. Achievements and perspectives.

Group-induced vector spaces

Pairwise feature evaluation for constructing reduced representations

Outlier detection using ball descriptions with adjustable metric
Sometimesnoveloroutlierdatahastobedetected.Theoutliersmayindicatesomeinterestingrareevent,ortheyshouldbedisregardedbecausetheycannotbereliablyprocessedfurther.Intheidealcasethattheobjectsarerepresentedbyverygoodfeatures,thegenuinedataformsacompactclusterandagoodoutliermeasureisth ...

Non-Euclidean or non-metric measures can be informative
StatisticallearningalgorithmsoftenrelyontheEuclideandistance.Inpractice,non-Euclideanornon-metricdissimilaritymeasuresmayarisewhencontours,spectraorshapesarecomparedbyeditdistancesorasaconsequenceofrobustobjectmatching[1,2].Itisanopenissuewhethersuchmeasuresareadvantageousforstat ...

Transforming strings to vector spaces using prototype selection
A common way of expressing string similarity in structural pattern recognition is the edit distance. It allows one to apply the kNN rule in order to classify a set of strings. However, compared to the wide range of elaborated classi¿ers known from statistical pattern recognition, ...

Object representation, sample size, and data set complexity

Dissimilarity-based classification for vectorial representations

Structural inference of sensor-based measurements
Statisticalinferenceofsensor-basedmeasurementsisintensivelystudiedinpatternrecognition.Itisusuallybasedonfeaturerepresentationsoftheobjectstoberecognized.Suchrepresentations,however,neglecttheobjectstructure.Structuralpatternrecognition,onthecontrary,focussesonencodingtheobjectst ...