One or more hypotheses may be induced from any set of exemplars and non exemplars. Incremental Modification of Hypothesis Fragments (IMHF) is a new algorithm for processing instances of concepts incrementally, discovering a consistent hypothesis after presentation of each example. / A modular connectionist approach using the back-propagation learning algorithm was taken to implement the IMHF algorithm. A shell called Parallel Unconnected Neural Networks (PUNN) was developed to give back-propagation the additional power of modularity and provided for the needed complexity to model IMHF. The PUNN implementation of the IMHF algorithm yielded a model of human induction of hypotheses from examples.

Dynamic Bayesian Networks (DBNs) are temporal probabilistic models for reasoning over time. They often formulate the core reasoning component of intelligent systems in the field of machine learning. Recent studies have focused on the development of some DBNs such as Hidden Markov Models (HMMs) and their variants, which are explicitly represented by highly skilled users and have gained popularity in speech recognition. These varieties of HMMs represented as DBNs have contributed to the baseline of temporal modelling. However they are limited in their expressive power as they are approximated and pose difficult challenges for users when choosing the appropriate model for diverse real-life applications. To worsen the situation further, researchers and practitioners have also stressed that applications often have difficulties when evolving (or learning) such network models from environments captured as massive datasets, due to the ongoing predominance of computational intensity (or nondeterministic polynomial (NP) time hard). Finding solutions to these challenges is a difficult task. In this thesis, a new class of temporal probabilistic modelling, called evolving dynamic Bayesian networks (EDBN), is proposed and demonstrated to make technology easier so as to accommodate both experts and non-experts, such as industrial practitioners, decision-makers, researchers, etc. Dynamic Bayesian Networks (DBNs) are ideally suited to achieve situation awareness, in which elements in the environment must be perceived within a volume of time and space, their meaning understood, and their status predicted in the near future. The use of Dynamic Bayesian Networks in achieving situation awareness has been poorly explored in current research efforts. This research completely evolves DBNs automatically from any environment captured as multivariate time series (MTS) which minimizes the approximations and mitigates the challenges of choice of models. This potentially accommodates both highly skilled users and non-expert practitioners, and attracts diverse real-world application areas for DBNs. The architecture of our EDBN uses a combined strategy as it resolves two orthogonal issues to address the challenging problems: (1) evolving DBNs in the absence of domain experts and (2) mitigating computational intensity (or NP-hard) problems with economic scalability. Most notably, the major contributions of this thesis are as follows: the development of a new class of temporal probabilistic modeling (EDBN), whose architecture facilitates the demonstration of its emergent situation awareness (ESA) and emergent future situation awareness (EFSA) technologies. The ESA and its variant reveal hidden patterns over current and future time steps respectively. Among other contributions are the development and integration of an economic scalable framework called dynamic memory management in adaptive learning (DMMAL) into the architecture of the EDBN to emerge such network models from environments captured as massive datasets; the design of configurable agent actuators; adaptive operators; representative partitioning algorithms which facilitate the scalability framework; formal development and optimization of genetic algorithm (GA) to emerge optimal Bayesian networks from datasets, with emphasis on backtracking avoidance; and diverse applications of EDBN technologies such as business intelligence, revealing trends of insulin dose to medical patients, water quality management, project profitability analysis, sensor networks, etc. To ensure the universality and reproducibility of our architecture, we methodically conducted experiments using varied real-life datasets and publicly available machine learning datasets mostly from the University of California Irvine (UCI) repository.

<p> With billions of internet users, online media services have become commonplace. Prediction and recommendation for online media are fundamental problems in various applications, including recommender systems and information retrieval. As an example, accurately predicting user behaviors improves user experiences through more intelligent user interfaces. On the other hand, user behavior prediction in online media is also strongly related to behavior targeting and online advertisement which is the major business for most consumer internet services. Estimating and understanding users' click behaviors is a critical problem in online advertising. In this dissertation, we investigate the prediction and recommendation problems in various online media. We find a number of challenges: high order relations, temporal dynamics, complexity of network structure, high data sparsity and coupled social media activities. We consider user behavior understanding and prediction in four areas: tag prediction in a social tagging system, link prediction in microblogging services, multi-context modeling in online social media and click prediction in sponsored search. In such topics, based on real world data, we analyze user behaviors and discover patterns, properties and challenges. Subsequently, we design specific models for online user behavior prediction in various online media: a probabilistic model for personalized tag prediction, a user-tag-specific temporal interests model for tracking users' interests over time in tagging systems, a personalized structure based link prediction model for micro-blogging systems, a generalized latent factor model and Bayesian treatment for modeling across multiple contexts in online social media, a context-aware click model and framework for estimating ad group performance in sponsored search. Our extensive experiments on large-scale real-world datasets show our novel models advance the state-of-the-art.</p>

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This work examines which audio features, the components of recorded sound, are most relevant to trumpet tone quality by using classification and feature selection. A total of 10 trumpet players with a variety of experience levels were recorded playing the same notes under the same conditions. Twelve musical instrumentalists listened to the notes and provided subjective ratings of the tone quality on a seven-point Likert scale to provide training data for classification. The initial experiment verified that there is statistical agreement between human raters on tone quality and that it was possible to train a support vector machine (SVM) classifier to identify different levels of tone quality with success of 72% classification accuracy with the notes split into two classes and 46% when using seven classes. In the main experiment, different types of feature selection algorithms were applied to the 164 possible audio features to select high-performing subsets. The baseline set of all 164 audio features obtained a classification accuracy of 58.9% with seven classes tested with cross-validation. Ranking, sequential floating forward selection, and genetic search produced accuracies of 43.8%, 53.6%, and 59.6% with 20, 21, and 74 features, respectively. Future work in this field could focus on more nuanced interpretations of tone quality or on the applicability to other instruments. / Ce travail examine les caractéristique acoustique, c.-à-d. les composantes de l'enregistrement sonore, les plus pertinentes pour la qualité du timbre de trompette à l'aide de la classification automatique et de la sélection de caractéristiques. Un total de 10 joueurs de trompette de niveau varié, jouant les mêmes notes dans les mêmes conditions, a été enregistré. Douze instrumentistes de musique ont écouté les enregistrements et ont fourni des évaluations subjectives de la qualité du timbre sur une échelle de Likert à sept points afin de fournir des données d'entrainement du système de classification. La première expérience a vérifié qu'il existe une correlation statistique entre les évaluateurs humains sur la qualité du timbre et qu'il était possible de former un système de classification de type machine à vecteurs de support pour identifier les différents niveaux de qualité du timbre avec un succès de précision de la classification de 72% pour les notes quand divisées en deux classes et 46% lors de l'utilisation de sept classes. Dans l'expérience principale, différents types d'algorithmes de sélection de caractéristiques ont été appliqués aux 164 fonctions au- dio possibles pour sélectionner les sous-ensembles les plus performants. L'ensemble de toutes les 164 fonctions audio a obtenu une précision de classification de 58,9% avec sept classes testées par validation croisée. Les algorithmes de "ranking," "sequential floating forward selection," et génétiques produisent une précision respective de 43,8%, 53,6% et 59,6% avec 20, 21 et 74 caractéristiques. Les futurs travaux dans ce domaine pourraient se concentrer sur des interprétations plus nuancées de la qualité du timbre ou sur l'applicabilité à d'autres instruments.

Decision making formulated as finding a strategy that maximizes a utility function depends critically on knowing the problem parameters precisely. The obtained strategy can be highly sub-optimal and/or infeasible when parameters are subject to uncertainty, a typical situation in practice. Robust optimization, and more generally robust decision making, addresses this issue by treating uncertain parameters as an arbitrary element of a pre-defined set and solving solutions based on a worst-case analysis. In this thesis we contribute to two closely related fields of robust decision making. First, we address two limitations of robust decision making. Namely, a lack of theoretical justification and conservatism in sequential decision making. Specifically, we provide an axiomatic justification of robust optimization based on the MaxMin Expected Utility framework from decision theory. Furthermore, we propose three less conservative decision criteria for sequential decision making tasks, which include: (1) In uncertain Markov decision processes we propose an alternative formulation of the parameter uncertainty -- the nested-set structured parameter uncertainty -- and find the strategy that achieves maxmin expected utility to mitigate the conservatism of the standard robust Markov decision processes. (2) We investigate uncertain Markov decision processes where each strategy is evaluated comparatively by its gap to the optimum value. Two formulations, namely minimax regret and mean-variance tradeoff of the regret, were proposed and their computational cost studied. (3) We propose a novel Kalman filter design based on trading-off the likely performance and the robustness under parameter uncertainty. Second, we apply robust decision making into machine learning both theoretically and algorithmically. Specifically, on the theoretical front, we show that the concept of robustness is essential to ''successful'' learning / La prise de décision, formulée comme trouver une stratégie qui maximise une fonction de l'utilité, dépend de manière critique sur la connaissance précise des paramètres du problem. La stratégie obtenue peut être très sous-optimale et/ou infeasible quand les paramètres sont subjets à l'incertitude – une situation typique en pratique. L'optimisation robuste, et plus genéralement, la prise de décision robuste, vise cette question en traitant le paramètre incertain comme un élement arbitraire d'un ensemble prédéfini et en trouvant une solution en suivant l'analyse du pire scénario. Dans cette thèse, nous contribuons envers deux champs intimement reliés et appartenant à la prise de décision robuste. En premier lieu, nous considérons deux limites de la prise de décision robuste: le manque de justification théorique et le conservatism dans la prise de décision séquentielle. Pour être plus spécifique, nous donnons une justifiquation axiomatique de l'optimisation robuste basée sur le cadre de l'utilité espérée MaxMin de la théorie de la prise de décision. De plus, nous proposons trois critères moins conservateurs pour la prise de décision séquentielle, incluant: (1) dans les processus incertains de décisionde Markov, nous proposons un modèle alternative de l'incertitude de paramètres –l'incertitude structurée comme des ensembles emboîtées – et trouvons une stratégie qui obtient une utilité espérée maxmin pour mitiguer le conservatisme des processus incertains de décision de Markov qui sont de norme. (2) Nous considérons les processus incertains de décision de Markov où chaque stratégie est évaluée par comparaison de l'écart avec l'optimum. Deux modèles – le regret minimax et le compromis entre l'espérance et la variance du regret – sont présentés et leurs complexités étudiées. (3)Nous proposons une nouvelle conception de filtre de Kalman b

Speaker-independence and large lexicon access are still two of the greatest problems in automatic speech recognition. Cognitive and information-theory approaches try to solve the recognition problem by proceeding in almost opposite directions. The former rely on knowledge representation, reasoning and perceptual analysis, while the latter is in general based on highly numerical and mathematical algorithms. / Progress arises from the integration of the two mentioned approaches. Artificial intelligence techniques are often used in the cognitive approach, but these techniques usually lack sophisticated numerical support. The Extended Procedural Network constitutes a general AI framework which supports powerful numerical strategies which include stochastic techniques. / The model has been tested on difficult problems in speech recognition, including speaker-independent letter and digit recognition, speaker-independent vowel and diphthong recognition, and access to a large lexicon. / Various experiments and comparisons have been run on a large number of speakers and the results are reported. / A discussion of further research advancements and investigations is provided.