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This thesis presents the design and prototype implementation of a digital twin based on a knowledge graph for Internet of Things (IoT) platforms. The digital twin is a virtual representation of a physical object or system that must continually integrate and update knowledge in rapidly changing environments. The proposed knowledge graph is designed to store and efficiently query a large number of IoT devices in a complex logical structure, use rule-based reasoning to infer new facts, and integrate unanticipated devices into the existing logical structure in order to adapt to changing environments. The digital twin is implemented using the open-source TypeDB knowledge graph and tested in a simplified automobile production line environment. The main focus of the work is on the integration of unanticipated devices, for which a similarity metric is implemented to identify similar existing devices and determine the appropriate integration into the knowledge graph. The proposed digital twin knowledge graph is a promising solution for managing and integrating knowledge in rapidly changing IoT environments, providing valuable insights and support for decision-making., I den här avhandlingen presenteras utformningen och prototypimplementeringen av en digital tvilling baserad på en kunskapsgraf för IoT-plattformar (Internet of Things). Den digitala tvillingen är en virtuell representation av ett fysiskt objekt eller system som måste integrera och uppdatera kunskap i snabbt föränderliga miljöer. Den föreslagna kunskapsgrafen är utformad för att lagra och effektivt söka efter en stor uppsättning IoT-enheter i en komplex logisk struktur, använda regelbaserade resonemang för att härleda nya fakta och integrera oväntade enheter i den befintliga logiska strukturen för att anpassa sig till föränderliga miljöer. Den digitala tvillingen genomförs med hjälp av kunskapsgrafen TypeDB med öppen källkod och testas i en förenklad miljö för bilproduktion. Huvudfokus ligger på integrationen av oväntade enheter, för vilka ett likhetsmått implementeras för att identifiera liknande befintliga enheter och bestämma lämplig integration i kunskapsgrafen. Den föreslagna kunskapsgrafen för digitala tvillingar är en lovande lösning för att hantera och integrera kunskap i snabbt föränderliga IoT-miljöer, vilket ger värdefulla insikter och stöd för beslutsfattande., Esta tesis presenta el diseño e implementación de un prototipo de gemelo digital basado en un grafo de conocimiento para plataformas de Internet de las Cosas (IoT). El gemelo digital es una representación virtual de un objeto o sistema físico que debe integrar y actualizar continuamente el conocimiento en entornos que cambian rápidamente. El grafo de conocimiento propuesto está diseñado para almacenar y consultar eficientemente un gran número de dispositivos IoT en una estructura lógica compleja, utilizar el razonamiento basado en reglas para inferir nuevos hechos e integrar dispositivos imprevistos en la estructura lógica existente para adaptarse a los cambios del entorno. El gemelo digital se implementa utilizando el grafo de conocimiento de código abierto TypeDB y se prueba en un entorno simplificado basado en una línea de producción de automóviles. El objetivo principal del trabajo es la integración de dispositivos no previstos, para lo cual se implementa una métrica de similitud para identificar dispositivos existentes similares y determinar la integración adecuada en el grafo de conocimiento. El grafo de conocimiento propuesto es una solución prometedora para la gestión del conocimiento y la integración en entornos IoT que cambian rápidamente, proporcionando información valiosa y apoyo a la toma de decisiones.

This thesis presents the design and prototype implementation of a digital twin based on a knowledge graph for Internet of Things (IoT) platforms. The digital twin is a virtual representation of a physical object or system that must continually integrate and update knowledge in rapidly changing environments. The proposed knowledge graph is designed to store and efficiently query a large number of IoT devices in a complex logical structure, use rule-based reasoning to infer new facts, and integrate unanticipated devices into the existing logical structure in order to adapt to changing environments. The digital twin is implemented using the open-source TypeDB knowledge graph and tested in a simplified automobile production line environment. The main focus of the work is on the integration of unanticipated devices, for which a similarity metric is implemented to identify similar existing devices and determine the appropriate integration into the knowledge graph. The proposed digital twin knowledge graph is a promising solution for managing and integrating knowledge in rapidly changing IoT environments, providing valuable insights and support for decision-making., I den här avhandlingen presenteras utformningen och prototypimplementeringen av en digital tvilling baserad på en kunskapsgraf för IoT-plattformar (Internet of Things). Den digitala tvillingen är en virtuell representation av ett fysiskt objekt eller system som måste integrera och uppdatera kunskap i snabbt föränderliga miljöer. Den föreslagna kunskapsgrafen är utformad för att lagra och effektivt söka efter en stor uppsättning IoT-enheter i en komplex logisk struktur, använda regelbaserade resonemang för att härleda nya fakta och integrera oväntade enheter i den befintliga logiska strukturen för att anpassa sig till föränderliga miljöer. Den digitala tvillingen genomförs med hjälp av kunskapsgrafen TypeDB med öppen källkod och testas i en förenklad miljö för bilproduktion. Huvudfokus ligger på integrationen av oväntade enheter, för vilka ett likhetsmått implementeras för att identifiera liknande befintliga enheter och bestämma lämplig integration i kunskapsgrafen. Den föreslagna kunskapsgrafen för digitala tvillingar är en lovande lösning för att hantera och integrera kunskap i snabbt föränderliga IoT-miljöer, vilket ger värdefulla insikter och stöd för beslutsfattande., Esta tesis presenta el diseño e implementación de un prototipo de gemelo digital basado en un grafo de conocimiento para plataformas de Internet de las Cosas (IoT). El gemelo digital es una representación virtual de un objeto o sistema físico que debe integrar y actualizar continuamente el conocimiento en entornos que cambian rápidamente. El grafo de conocimiento propuesto está diseñado para almacenar y consultar eficientemente un gran número de dispositivos IoT en una estructura lógica compleja, utilizar el razonamiento basado en reglas para inferir nuevos hechos e integrar dispositivos imprevistos en la estructura lógica existente para adaptarse a los cambios del entorno. El gemelo digital se implementa utilizando el grafo de conocimiento de código abierto TypeDB y se prueba en un entorno simplificado basado en una línea de producción de automóviles. El objetivo principal del trabajo es la integración de dispositivos no previstos, para lo cual se implementa una métrica de similitud para identificar dispositivos existentes similares y determinar la integración adecuada en el grafo de conocimiento. El grafo de conocimiento propuesto es una solución prometedora para la gestión del conocimiento y la integración en entornos IoT que cambian rápidamente, proporcionando información valiosa y apoyo a la toma de decisiones.

Augmented Intelligence (AuI) refers to the use of artificial intelligence (AI) to amplify certain cognitive tasks performed by human decision-makers. However, there are concerns that AI’s increasing capability and alignment with human values may undermine user agency, autonomy, and responsible decision-making. To address these concerns, we conducted a user study in the field of orthopedic radiology diagnosis, introducing a reflective XAI (explainable AI) support that aimed to stimulate human reflection, and we evaluated its impact of in terms of decision performance, decision confidence and perceived utility. Specifically, the reflective XAI support system prompted users to reflect on the dependability of AI-generated advice by presenting evidence both in favor of and against its recommendation. This evidence was presented via two cases that closely resembled a given base case, along with pixel attribution maps. These cases were associated with the same AI advice for the base case, but one case was accurate while the other was erroneous with respect to the ground truth. While the introduction of this support system did not significantly enhance diagnostic accuracy, it was highly valued by more experienced users. Based on the findings of this study, we advocate for further research to validate the potential of reflective XAI in fostering more informed and responsible decision-making, ultimately preserving human agency.

Simulation models that replicate realistic crowd behaviours and dynamics are of great societal use in a variety of fields of research and entertainment. In order to evaluate the accuracy of such models there is a demand for metrics and evaluation solutions that measure how well they simulate the dynamics of real crowds. A crowd similarity metric is a performance indicator which quantifies the similarity of crowd trajectories. Similarity metrics may be used to evaluate the validity of simulation models by comparing the content they produce to real-world crowd trajectory data. This thesis presents and evaluates a similarity metric which employs an approach based on the Sum of Absolute Differences to compare two-dimensional crowd trajectories. The metric encapsulates the similarity of crowd trajectories by iteratively summing time-wise positional differences on a per-agent basis. The resulting metric is simple, highly reproducible and simulatorindependent. Its accuracy in quantifying similarity is evaluated by means of a user study investigating the correlation between metric values and human perception of similarity for real and simulated crowd scenarios of varying density, trajectory, speed, and presence of environmental obstacles. The user study explores different aspects of crowd perception by dividing similarity ratings on a five-point Likert scale into four categories: overall, in terms of trajectories, speeds, and positions. Scenarios and rating categories that indicate high and low degrees of correlation between metric values and perceived similarity are identified and discussed. Furthermore, the findings are compared to previous research on crowd trajectory similarity metrics. The results indicate that the metric shows promising potential for accurate similarity measurement in simple and sparse scenarios across all rated categories. Moreover, the metric is strongly correlated with the trajectory-ratings of crowd motion similarity. However, it appears to not co, Simuleringsmodeller som efterhärmar realistiskt beteende och dynamik bland folkmassor är av stor samhällelig nytta i flertalet forskningsområden och i underhållningsbranschen. För att utvärdera sådana modellers noggrannhet finns det en efterfrågan av metriker och bedömningslösningar som mäter hur väl modeller simulerar verklig folkmassadynamik. En likhetsmetrik för folkmassautvärdering är en prestationsindikator som kvantifierar likheten mellan folkmassarörelser. Likhetsmetriker kan användas för att utvärdera simuleringsmodellers validitet genom att jämföra beteendet de producerar med rörelsedata från verkliga folkmassor. Följande examensarbete presenterar och utvärderar en likhetsmetrik för folkmassor som utnyttjar en metod baserad på ”Summan av Absoluta Skillnader” för att jämföra par av två-dimensionella folkmassarörelser. Metriken uppskattar likheten mellan två folkmassors rörelser genom att iterativt summera skillnaderna mellan folkmassornas positioner baserat på de individuella virtuella agenterna. Resultatet är en simpel, kraftigt reproducerbar och simulator-oberoende metrik. Dess noggrannhet avseende likhetsestimering utvärderas med en perceptuell användarstudie som undersöker korrelationen mellan metrikvärden och mänsklig perception av visuell likhet för flertalet verkliga och simulerade folkmassor av varierande densitet, färdväg, hastighet, och förekomst av hinder. Användarstudien utforskar olika aspekter av folkmassaperception genom att dela upp likhetsgradering på en femgradig skala i fyra kategorier: övergripande, avseende färdvägar, hastigheter, och positioner. Folkmassascenarier och graderingskategorier som indikerar höga och låga nivåer av korrelation mellan metrikvärden och uppfattad likhet identifieras och diskuteras. Fortsättningsvis jämförs resultaten med tidigare forskning om likhetsmetriker för folkmassautvärdering. Resultaten tyder på att metriken har lovande potential för noggrann likhetsestimering i enkla och glesa scenarier oavsett graderin

Trace similarity is a prerequisite for several process mining tasks, e.g. identifying process variants and anomalies. Many similarity metrics have been presented in the literature, but the similarity metric itself is seldom subject to controlled evaluation. Instead, they are usually demonstrated in conjunction with downstream tasks, e.g. process model discovery, and often evaluated qualitatively or with limited comparison. In this paper, we isolate similarity metrics from downstream tasks and compare them wrt. evaluation measures adapted from metric learning and clustering literature. We present a comparison of 18 similarity metrics across 4 evaluation measures and 12 event logs. Friedman and Nemenyi tests for statistical significance show that certain similarity metrics consistently outperform on some evaluation measures, but their mean rank varies across evaluation measures. One similarity metric based on a weighted eventually-follows relation does stand out as consistently outperforming, and the simplest n-gram similarity metrics also perform well. Our results demonstrate that choice of evaluation measures will determine the contours of the metric that are revealed. This study may be harnessed as a baseline for benchmarking future work on trace similarity, and describes tools for quantitative evaluation that we hope will inspire empirical rigor in future work.

We introduce a new mathematical basis for similarity space. For the first time, we describe the relationship between distance and similarity from set theory. Then, we derive generally valid relations for the conversion between similarity and a metric and vice versa. We present a general solution for the normalization of a given similarity space or metric space. The derived solutions lead to many already used similarity and distance functions, and combine them into a unified theory. The Jaccard coefficient, Tanimoto coefficient, Steinhaus distance, Ruzicka similarity, Gaussian similarity, edit distance and edit similarity satisfy this relationship, which verifies our fundamental theory., Zavádíme nový matematický základ pro prostor podobnosti. Poprvé popisujeme vztah mezi vzdáleností a podobností z teorie množin. Poté odvozujeme obecně platné vztahy pro převod mezi podobností a metrikou a naopak. Uvádíme obecné řešení normalizace daného prostoru podobnosti nebo metrického prostoru. Odvozená řešení vedou k mnoha již používaným funkcím podobnosti a vzdálenosti a spojují je do jednotné teorie. Tento vztah splňují Jaccardův koeficient, Tanimotův koeficient, Steinhausova vzdálenost, Ruzickova podobnost, Gaussova podobnost, editační vzdálenost a editační podobnost, což ověřuje naši základní teorii.

In this paper, we introduce a deformation based representation space for curved shapes in Rn. Given an ordered set of points sampled from a curved shape, the proposed method represents the set as an element of a finite dimensional matrix Lie group. Variation due to scale and location are filtered in a preprocessing stage, while shapes that vary only in rotation are identified by an equivalence relationship. The use of a finite dimensional matrix Lie group leads to a similarity metric with an explicit geodesic solution. Subsequently, we discuss some of the properties of the metric and its relationship with a deformation by least action. Furthermore, invariance to reparametrization or estimation of point correspondence between shapes is formulated as an estimation of sampling function. Thereafter, two possible approaches are presented to solve the point correspondence estimation problem. Finally, we propose an adaptation of k-means clustering for shape analysis in the proposed representation space. Experimental results show that the proposed representation is robust to uninformative cues, e.g. local shape perturbation and displacement. In comparison to state of the art methods, it achieves a high precision on the Swedish and the Flavia leaf datasets and a comparable result on MPEG-7, Kimia99 and Kimia216 datasets.

In this paper, we introduce a deformation based representation space for curved shapes in Rn. Given an ordered set of points sampled from a curved shape, the proposed method represents the set as an element of a finite dimensional matrix Lie group. Variation due to scale and location are filtered in a preprocessing stage, while shapes that vary only in rotation are identified by an equivalence relationship. The use of a finite dimensional matrix Lie group leads to a similarity metric with an explicit geodesic solution. Subsequently, we discuss some of the properties of the metric and its relationship with a deformation by least action. Furthermore, invariance to reparametrization or estimation of point correspondence between shapes is formulated as an estimation of sampling function. Thereafter, two possible approaches are presented to solve the point correspondence estimation problem. Finally, we propose an adaptation of k-means clustering for shape analysis in the proposed representation space. Experimental results show that the proposed representation is robust to uninformative cues, e.g. local shape perturbation and displacement. In comparison to state of the art methods, it achieves a high precision on the Swedish and the Flavia leaf datasets and a comparable result on MPEG-7, Kimia99 and Kimia216 datasets.

Reddit (www.reddit.com) is a social news platform for information sharing and exchanging. The amount of data, in terms of both observations and dimensions is enormous because a large number of users express all aspects of knowledge in their own lives by publishing the comments. While it’s easy for a human being to understand the Reddit comments on an individual basis, it is a tremendous challenge to use a computer and extract insights from it. In this thesis, we seek one algorithmic driven approach to analyze both the unique Reddit data structure and the relations inside owners of comments by their similar features. We explore the various types of communications between two people with common characteristics and build a special communication model that characterizes the potential relationship between two users via their communication messages. We then seek a dimensionality reduction methodology that can merge users with similar behavior into same groups. Along the process, we develop computer program to collect data, define attributes based on the communication model and apply a rule-based group merging algorithm. We then evaluate the results to show the effectiveness of this methodology. Our results show reasonable success in producing user groups that have recognizable group characteristics and share similar intentions.

Reddit (www.reddit.com) is a social news platform for information sharing and exchanging. The amount of data, in terms of both observations and dimensions is enormous because a large number of users express all aspects of knowledge in their own lives by publishing the comments. While it’s easy for a human being to understand the Reddit comments on an individual basis, it is a tremendous challenge to use a computer and extract insights from it. In this thesis, we seek one algorithmic driven approach to analyze both the unique Reddit data structure and the relations inside owners of comments by their similar features. We explore the various types of communications between two people with common characteristics and build a special communication model that characterizes the potential relationship between two users via their communication messages. We then seek a dimensionality reduction methodology that can merge users with similar behavior into same groups. Along the process, we develop computer program to collect data, define attributes based on the communication model and apply a rule-based group merging algorithm. We then evaluate the results to show the effectiveness of this methodology. Our results show reasonable success in producing user groups that have recognizable group characteristics and share similar intentions.

Communities of academic authors are usually identified by means of standard community detection algorithms, which exploit 'static' relations, such as co-authorship or citation networks. In contrast with these approaches, here we focus on diachronic topic-based communities -i.e., communities of people who appear to work on semantically related topics at the same time. These communities are interesting because their analysis allows us to make sense of the dynamics of the research world -e.g., migration of researchers from one topic to another, new communities being spawn by older ones, communities splitting, merging, ceasing to exist, etc. To this purpose, we are interested in developing clustering methods that are able to handle correctly the dynamic aspects of topic-based community formation, prioritizing the relationship between researchers who appear to follow the same research trajectories. We thus present a novel approach called Temporal Semantic Topic-Based Clustering (TST), which exploits a novel metric for clustering researchers according to their research trajectories, defined as distributions of semantic topics over time. The approach has been evaluated through an empirical study involving 25 experts from the Semantic Web and Human-Computer Interaction areas. The evaluation shows that TST exhibits a performance comparable to the one achieved by human experts. © 2014 Springer International Publishing.

Most of the works in the photoelasticity field have been focused on the stress distribution study for birefringent materials of a defined thickness, due to the complexity in the relationship between the material thickness, the refractive indices and the phase delay generated in the transmission of light. There are cases where occur temporal change on the relationship presented above while the force is applied, cases where a make a description of the photoelasticity phenomenon is more difficult by the complexity of it, the deformation of plastic films is one of these cases. This paper presents a technique based in similarity metrics for the image analysis on photoelasticity studies of plastic film deformation under mechanical stress. The plastic film is deformed in a mechanical traction machine, the photoelasticity images are captured in a video camera through of optical assembly. The dynamic behavior of the mechanical deformation in the plastic film can be related whit dynamics behavior obtained in the similarity metrics, La mayoría de los trabajos realizados en el campo de la fotoelasticidad se han centrado en el estudio de la distribución de esfuerzos en materiales birrefringentes de espesor definido debido a la complejidad que presenta la relación entre el espesor del material, los índices de refracción y el retardo de fase que se genera en la transmisión de la luz. Existen casos donde la relación antes mencionada presenta cambios en la medida que se aplica fuerza al material en estudio, casos donde realizar una descripción del fenómeno fotoelástico es de mayor dificultad por la complejidad del mismo; la deformación de películas plásticas es uno de estos casos. Este trabajo presenta una técnica basada en métricas de similitud para el análisis de las imágenes obtenidas en estudios de fotoelasticidad de películas plásticas sometidas a deformación por esfuerzos mecánicos. La película plástica es deformada en una máquina de tracción mecánica, las imágenes de fotoelasticidad son capturadas en una cámara de vídeo a través de un montaje óptico. Los resultados del comportamiento mecánico de la deformación del material plástico presentan una dinámica que puede ser relacionada con la dinámica obtenida en las métricas de similitud

Most of the works in the photoelasticity field have been focused on the stress distribution study for birefringent materials of a defined thickness, due to the complexity in the relationship between the material thickness, the refractive indices and the phase delay generated in the transmission of light. There are cases where occur temporal change on the relationship presented above while the force is applied, cases where a make a description of the photoelasticity phenomenon is more difficult by the complexity of it, the deformation of plastic films is one of these cases. This paper presents a technique based in similarity metrics for the image analysis on photoelasticity studies of plastic film deformation under mechanical stress. The plastic film is deformed in a mechanical traction machine, the photoelasticity images are captured in a video camera through of optical assembly. The dynamic behavior of the mechanical deformation in the plastic film can be related whit dynamics behavior obtained in the similarity metrics, La mayoría de los trabajos realizados en el campo de la fotoelasticidad se han centrado en el estudio de la distribución de esfuerzos en materiales birrefringentes de espesor definido debido a la complejidad que presenta la relación entre el espesor del material, los índices de refracción y el retardo de fase que se genera en la transmisión de la luz. Existen casos donde la relación antes mencionada presenta cambios en la medida que se aplica fuerza al material en estudio, casos donde realizar una descripción del fenómeno fotoelástico es de mayor dificultad por la complejidad del mismo; la deformación de películas plásticas es uno de estos casos. Este trabajo presenta una técnica basada en métricas de similitud para el análisis de las imágenes obtenidas en estudios de fotoelasticidad de películas plásticas sometidas a deformación por esfuerzos mecánicos. La película plástica es deformada en una máquina de tracción mecánica, las imágenes de fotoelasticidad son capturadas en una cámara de vídeo a través de un montaje óptico. Los resultados del comportamiento mecánico de la deformación del material plástico presentan una dinámica que puede ser relacionada con la dinámica obtenida en las métricas de similitud

Most of the works in the photoelasticity field have been focused on the stress distribution study for birefringent materials of a defined thickness, due to the complexity in the relationship between the material thickness, the refractive indices and the phase delay generated in the transmission of light. There are cases where occur temporal change on the relationship presented above while the force is applied, cases where a make a description of the photoelasticity phenomenon is more difficult by the complexity of it, the deformation of plastic films is one of these cases. This paper presents a technique based in similarity metrics for the image analysis on photoelasticity studies of plastic film deformation under mechanical stress. The plastic film is deformed in a mechanical traction machine, the photoelasticity images are captured in a video camera through of optical assembly. The dynamic behavior of the mechanical deformation in the plastic film can be related whit dynamics behavior obtained in the similarity metrics, La mayoría de los trabajos realizados en el campo de la fotoelasticidad se han centrado en el estudio de la distribución de esfuerzos en materiales birrefringentes de espesor definido debido a la complejidad que presenta la relación entre el espesor del material, los índices de refracción y el retardo de fase que se genera en la transmisión de la luz. Existen casos donde la relación antes mencionada presenta cambios en la medida que se aplica fuerza al material en estudio, casos donde realizar una descripción del fenómeno fotoelástico es de mayor dificultad por la complejidad del mismo; la deformación de películas plásticas es uno de estos casos. Este trabajo presenta una técnica basada en métricas de similitud para el análisis de las imágenes obtenidas en estudios de fotoelasticidad de películas plásticas sometidas a deformación por esfuerzos mecánicos. La película plástica es deformada en una máquina de tracción mecánica, las imágenes de fotoelasticidad son capturadas en una cámara de vídeo a través de un montaje óptico. Los resultados del comportamiento mecánico de la deformación del material plástico presentan una dinámica que puede ser relacionada con la dinámica obtenida en las métricas de similitud

An analysis of biobasis function neural networks is presented, which shows that the similarity metric used is a linear function and that bio-basis function neural networks therefore often end up being just linear classifiers in high dimensional spaces. This is a consequence of four things: the linearity of the distance measure, the normalization of the distance measure, the recommended default values of the parameters, and that biological data sets are sparse., ©2007 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

An analysis of biobasis function neural networks is presented, which shows that the similarity metric used is a linear function and that bio-basis function neural networks therefore often end up being just linear classifiers in high dimensional spaces. This is a consequence of four things: the linearity of the distance measure, the normalization of the distance measure, the recommended default values of the parameters, and that biological data sets are sparse., ©2007 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

An analysis of biobasis function neural networks is presented, which shows that the similarity metric used is a linear function and that bio-basis function neural networks therefore often end up being just linear classifiers in high dimensional spaces. This is a consequence of four things: the linearity of the distance measure, the normalization of the distance measure, the recommended default values of the parameters, and that biological data sets are sparse., ©2007 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

An analysis of biobasis function neural networks is presented, which shows that the similarity metric used is a linear function and that bio-basis function neural networks therefore often end up being just linear classifiers in high dimensional spaces. This is a consequence of four things: the linearity of the distance measure, the normalization of the distance measure, the recommended default values of the parameters, and that biological data sets are sparse., ©2007 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

An analysis of biobasis function neural networks is presented, which shows that the similarity metric used is a linear function and that bio-basis function neural networks therefore often end up being just linear classifiers in high dimensional spaces. This is a consequence of four things: the linearity of the distance measure, the normalization of the distance measure, the recommended default values of the parameters, and that biological data sets are sparse., ©2007 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

An analysis of biobasis function neural networks is presented, which shows that the similarity metric used is a linear function and that bio-basis function neural networks therefore often end up being just linear classifiers in high dimensional spaces. This is a consequence of four things: the linearity of the distance measure, the normalization of the distance measure, the recommended default values of the parameters, and that biological data sets are sparse., ©2007 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.