The importance of attribute vector ambiguity has been largely overlooked by the machine learning community. A pattern recognition problem can be solved in many ways within the scope of machine learning. Neural Networks, Decision Tree Algorithms such as C4.5, Bayesian Classifiers, and Instance Based Learning are the main algorithms. All listed solutions fail to address ambiguity in the attribute vector. The research reported shows, ignoring this ambiguity leads to problems of classifier scalability and issues with instance collection and aggregation. The Algorithm presented accounts for both ambiguity of the attribute vector and class label thus solving both issues of scalability and instance collection. The research also shows that when applied to sanitized data sets, suitable for traditional instance based learning, the presented algorithm performs equally as well.

No / In support of the increasing number of elderly population, wearable sensors and portable mobile devices capable of monitoring, recording, reporting and alerting are envisaged to enable them an independent lifestyle without relying on intrusive care programmes. However, the big data readings generated from the sensors are characterized as multidimensional, dynamic and non-linear with weak correlation with observable human behaviors and health conditions which challenges the information transmission, storing and processing. This paper proposes to use Locality Sensitive Bloom Filter to increase the Instance Based Learning efficiency for the front end sensor data pre-processing so that only relevant and meaningful information will be sent out for further processing aiming to relieve the burden of the above big data challenges. The approach is proven to optimize and enhance a popular instance-based learning method benefits from its faster speed, less space requirements and is adequate for the application.

Made available in DSpace on 2014-06-12T15:52:09Z (GMT). No. of bitstreams: 1 license.txt: 1748 bytes, checksum: 8a4605be74aa9ea9d79846c1fba20a33 (MD5) Previous issue date: 2008 / O sucesso de um software depende diretamente de sua qualidade. Tradicionalmente, métodos formais e de inspeção manual de código são usados para assegurá-la. Tais métodos, geralmente, possuem um custo elevado e demandam bastante tempo. Dessa forma, as atividades de teste devem ser planejadas cuidadosamente para evitar o desperdício de recursos. Atualmente, as organizações estão buscando maneiras rápidas e baratas de detectar defeitos em softwares. Porém, mesmo com todos os avanços dos últimos anos, o desenvolvimento de software ainda é uma atividade que depende intensivamente do esforço e do conhecimento humano. Muitos pesquisadores e organizações estão interessados em criar um mecanismo capaz de prever automaticamente defeitos em softwares. Nos últimos anos, técnicas de aprendizagem de máquina vêm sendo utilizadas em diversas pesquisas com esse objetivo. Este trabalho investiga e apresenta um estudo da viabilidade da aplicação de métodos de aprendizagem de máquina na detecção de módulos de software propensos a falhas. Classificadores como redes neurais artificiais e técnicas de aprendizagem baseada em instâncias (instance-based learning) serão usadas nessa tarefa, tendo como fonte de informação as métricas de software retiradas do repositório do Metrics Data Program (MDP) da NASA. Também será apresentado um conjunto de melhorias, propostas durante este trabalho, para alguns desses classificadores. Como a detecção de módulos defeituosos é um problema sensível a custo, este trabalho também propõe um mecanismo capaz de medir analiticamente o custo de cada decisão tomada pelos classificadores

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This thesis is specialized in instance based learning algorithms. Main goal is to create an application for educational purposes. There are instance based learning algorithms (IBL), nearest neighbor algorithms and kd-trees described theoretically in this thesis. Practical part is about making of tutorial application. Application can generate data, classified them with nearest neighbor algorithm and is able of IB1, IB2 and IB3 algorithm testing.

First chapter is about basic information pattern learning. Second chapter is about solutions of pattern recognition and about using artificial inteligence and there are basic informations about statistics and theory of chaos. Third chapter is focused on time series, types of time series and preprocessing. There are informations about time series in financial sector. Fourth charter discuss about pattern recognition problems and about prediction. Last charter is about software, which I did and there are informations about part sof program.

Nos travaux de thèses s’intéressent au pronostic de défaillance de composant critique et à l’estimation de la durée de vie résiduelle avant défaillance (RUL). Nous avons développé des méthodes basées sur l’expérience. Cette orientation nous permet de nous affranchir de la définition d’un seuil de défaillance, point problématique lors de l’estimation du RUL. Nous avons pris appui sur le paradigme de Raisonnement à Partir de Cas (R à PC) pour assurer le suivi d’un nouveau composant critique et prédire son RUL. Une approche basée sur les instances (IBL) a été développée en proposant plusieurs formalisations de l’expérience : une supervisée tenant compte de l’ état du composant sous forme d’indicateur de santé et une non-supervisée agrégeant les données capteurs en une série temporelle mono-dimensionnelle formant une trajectoire de dégradation. Nous avons ensuite fait évoluer cette approche en intégrant de la connaissance à ces instances. La connaissance est extraite à partir de données capteurs et est de deux types : temporelle qui complète la modélisation des instances et fréquentielle qui, associée à la mesure de similarité permet d’affiner la phase de remémoration. Cette dernière prend appui sur deux types de mesures : une pondérée entre fenêtres parallèles et fixes et une pondérée avec projection temporelle. Les fenêtres sont glissantes ce qui permet d’identifier et de localiser l’état actuel de la dégradation de nouveaux composants. Une autre approche orientée donnée a été test ée. Celle-ci est se base sur des caractéristiques extraites des expériences, qui sont mono-dimensionnelles dans le premier cas et multi-dimensionnelles autrement. Ces caractéristiques seront modélisées par un algorithme de régression à vecteurs de support (SVR). Ces approches ont été évaluées sur deux types de composants : les turboréacteurs et les batteries «Li-ion». Les résultats obtenus sont intéressants mais dépendent du type de données traitées. / Our thesis work is concerned with the development of experience based approachesfor criticalcomponent prognostics and Remaining Useful Life (RUL) estimation. This choice allows us to avoidthe problematic issue of setting a failure threshold.Our work was based on Case Based Reasoning (CBR) to track the health status of a new componentand predict its RUL. An Instance Based Learning (IBL) approach was first developed offering twoexperience formalizations. The first is a supervised method that takes into account the status of thecomponent and produces health indicators. The second is an unsupervised method that fuses thesensory data into degradation trajectories.The approach was then evolved by integrating knowledge. Knowledge is extracted from the sensorydata and is of two types: temporal that completes the modeling of instances and frequential that,along with the similarity measure refine the retrieval phase. The latter is based on two similaritymeasures: a weighted one between fixed parallel windows and a weighted similarity with temporalprojection through sliding windows which allow actual health status identification.Another data-driven technique was tested. This one is developed from features extracted from theexperiences that can be either mono or multi-dimensional. These features are modeled by a SupportVector Regression (SVR) algorithm. The developed approaches were assessed on two types ofcritical components: turbofans and ”Li-ion” batteries. The obtained results are interesting but theydepend on the type of the treated data.

This diploma work initially conduct research in the area of descriptions and analysis of time series. The thesis then proceed to introduce the problems of technical analysis of price charts as well as indicators, price patterns and method of Pure Price Action. The method Pure Price Action is demonstrated in this work in two practical examples of its application to real businesses with a view to discovering and analyzing price patterns, as well as analysis and prediction of future price and financial evolution. This analysis is an introduction to the processes of successful business, following on from this we discuss the theme of Pattern Recognition and the Instance Based Learning method. The practical aspect of this work is carried out with the aid of a MATLAB applied algorithm for the analysis of the price pattern Correction for sale and purchase in dynamic time segments, specifically in trading price graphs, like those used for commodities or stock trading. For the analysis of time series we use the Pure Price Action method. The Instance Based Learning method is used by the algorithm to recognize price patterns. The created algorithm is verified on real data of a 5 minute time series of the US Dow Jones price charts for the years 2006, 2007, 2008. The achieved accuracy is evaluated with the aid of Equity Curves.