18 research outputs found

    Meta Learning Approach to Phone Duration Modeling

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    One of the essential prerequisites for achieving the naturalness of synthesized speech is the possibility of the automatic prediction of phone duration, due to the high importance of segmental duration in speech perception. In this paper we present a new phone duration prediction model for the Serbian language using meta learning approach. Based on the data obtained from the analysis of a large speech database, we used a feature set of 21 parameters describing phones and their contexts. These include attributes related to the segmental identity, manner of articulation (for consonants), attributes related to phonological context, such as segment types and voicing values of neighboring phones, presence or absence of lexical stress, morphological attributes, such as part-of-speech, and prosodic attributes, such as phonological word length, the position of the segment in the syllable, the position of the syllable in a word, the position of a word in a phrase, phrase break level, etc. Phone duration model obtained using meta learning algorithm outperformed the best individual model by approximately 2,0% and 1,7% in terms of the relative reduction of the root-mean-squared error and the mean absolute error, respectively

    Simple low cost causal discovery using mutual information and domain knowledge

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    PhDThis thesis examines causal discovery within datasets, in particular observational datasets where normal experimental manipulation is not possible. A number of machine learning techniques are examined in relation to their use of knowledge and the insights they can provide regarding the situation under study. Their use of prior knowledge and the causal knowledge produced by the learners are examined. Current causal learning algorithms are discussed in terms of their strengths and limitations. The main contribution of the thesis is a new causal learner LUMIN that operates with a polynomial time complexity in both the number of variables and records examined. It makes no prior assumptions about the form of the relationships and is capable of making extensive use of available domain information. This learner is compared to a number of current learning algorithms and it is shown to be competitive with them

    Study on non-parametric methods for fast pattern recognition with emphasis on neural networks and cascade classifiers

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    Tese de doutoramento em Engenharia Eletrotécnica e de Computadores, no ramo de especialização em Automação e Robótica, apresentada à Faculdade de Ciências e Tecnologia da Universidade de CoimbraEsta tese concentra-se em reconhecimento de padrões, com particular ênfase para o con ito de escolha entre capacidade de generalização e custo computacional, a m de fornecer suporte para aplicações em tempo real. Neste contexto são apresentadas contribuições metodológicas e analíticas para a abordagem de dois tipos de datasets: balanceados e desbalanceados. Um dataset é denominado balanceado quando há um número aproximadamente igual de observações entre as classes, enquanto datasets que têm números desiguais de observações entre as classes são denominados desbalanceados, tal como ocorre no caso de detecção de objetos baseada em imagem. Para datasets balanceados é adoptado o perceptrão multicamada (MLP) como classi cador, uma vez que tal modelo é um aproximador universal, ou seja MLPs podem aproximar qualquer conjunto de dados. Portanto, ao invés de propor novos modelos de classi cadores, esta tese concentra-se no desenvolvimento e análise de novos métodos de treinamento para MLP, de forma a melhorar a sua capacidade de generalização através do estudo de quatro abordagens diferentes: maximização da margem de classi cação, redundância, regularização, e transdução. A idéia é explorar novos métodos de treino para MLP com vista a obter classi cadores não-lineares mais rápidos que o usual SVM com kernel não-linear, mas com capacidade de generalização similar. Devido à sua função de decisão, o SVM com kernel não-linear exige um esforço computacional elevado quando o número de vetores de suporte é grande. No contexto dos datasets desbalanceados, adotou-se classi cadores em cascata, já que tal modelo pode ser visto como uma árvore de decisão degenerativa que realiza rejeições em cascata, mantendo o tempo de processamento adequado para aplicações em tempo real. Tendo em conta que conjuntos de classi cadores são susceptíveis a ter alta dimensão VC, que pode levar ao over- tting dos dados de treino, foram deduzidos limites para a capacidade de generalização dos classi cadores em cascata, a m de suportar a aplicação do princípio da minimização do risco estrutural (SRM). Esta tese também apresenta contribuições na seleção de características e dados de treinamento, devido à forte in uência que o pre-processamento dos dados tem sobre o reconhecimento de padrões. Os métodos propostos nesta tese foram validados em vários datasets do banco de dados da UCI. Alguns resultados experimentais já podem ser consultados em três revistas da ISI, outros foram submetidos a duas revistas e ainda estão em processo de revisão. No entanto, o estudo de caso desta tese é limitado à detecção e classi cação de peões.This thesis focuses on pattern recognition, with particular emphasis on the trade o between generalization capability and computational cost, in order to provide support for on-the- y applications. Within this context, two types of datasets are analyzed: balanced and unbalanced. A dataset is categorized as balanced when there are approximately equal numbers of observations in the classes, while unbalanced datasets have unequal numbers of observations in the classes, such as occurs in case of imagebased object detection. For balanced datasets it is adopted the multilayer perceptron (MLP) as classi er, since such model is a universal approximator, i.e. MLPs can t any dataset. Therefore, rather than proposing new classi er models, this thesis focuses on developing and analysing new training methods for MLP, in order to improve its generalization capability by exploiting four di erent approaches: maximization of the classi cation margin, redundancy, regularization, and transduction. The idea is to exploit new training methods for MLP aiming at an nonlinear classi er faster than the usual SVM with nonlinear kernel, but with similar generalization capability. Note that, due to its decision function, the SVM with nonlinear kernel requires a high computational e ort when the number of support vectors is big. For unbalanced datasets it is adopted the cascade classi er scheme, since such model can be seen as a degenerate decision tree that performs sequential rejection, keeping the processing time suitable for on-the- y applications. Taking into account that classi er ensembles are likely to have high VC dimension, which may lead to over- tting the training data, it were derived generalization bounds for cascade classi ers, in order to support the application of structural risk minimization (SRM) principle. This thesis also presents contributions on feature and data selection, due to the strong in uence that data pre-processing has on pattern recognition. The methods proposed in this thesis were validated through experiments on several UCI benchmark datasets. Some experimental results can be found in three ISI journals, others has been already submitted to two ISI journals, and are under review. However, the case study of this thesis is limited to pedestrian detection and classi cation

    Fundamentals

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    Volume 1 establishes the foundations of this new field. It goes through all the steps from data collection, their summary and clustering, to different aspects of resource-aware learning, i.e., hardware, memory, energy, and communication awareness. Machine learning methods are inspected with respect to resource requirements and how to enhance scalability on diverse computing architectures ranging from embedded systems to large computing clusters

    Fundamentals

    Get PDF
    Volume 1 establishes the foundations of this new field. It goes through all the steps from data collection, their summary and clustering, to different aspects of resource-aware learning, i.e., hardware, memory, energy, and communication awareness. Machine learning methods are inspected with respect to resource requirements and how to enhance scalability on diverse computing architectures ranging from embedded systems to large computing clusters

    xxAI - Beyond Explainable AI

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    This is an open access book. Statistical machine learning (ML) has triggered a renaissance of artificial intelligence (AI). While the most successful ML models, including Deep Neural Networks (DNN), have developed better predictivity, they have become increasingly complex, at the expense of human interpretability (correlation vs. causality). The field of explainable AI (xAI) has emerged with the goal of creating tools and models that are both predictive and interpretable and understandable for humans. Explainable AI is receiving huge interest in the machine learning and AI research communities, across academia, industry, and government, and there is now an excellent opportunity to push towards successful explainable AI applications. This volume will help the research community to accelerate this process, to promote a more systematic use of explainable AI to improve models in diverse applications, and ultimately to better understand how current explainable AI methods need to be improved and what kind of theory of explainable AI is needed. After overviews of current methods and challenges, the editors include chapters that describe new developments in explainable AI. The contributions are from leading researchers in the field, drawn from both academia and industry, and many of the chapters take a clear interdisciplinary approach to problem-solving. The concepts discussed include explainability, causability, and AI interfaces with humans, and the applications include image processing, natural language, law, fairness, and climate science.https://digitalcommons.unomaha.edu/isqafacbooks/1000/thumbnail.jp

    xxAI - Beyond Explainable AI

    Get PDF
    This is an open access book. Statistical machine learning (ML) has triggered a renaissance of artificial intelligence (AI). While the most successful ML models, including Deep Neural Networks (DNN), have developed better predictivity, they have become increasingly complex, at the expense of human interpretability (correlation vs. causality). The field of explainable AI (xAI) has emerged with the goal of creating tools and models that are both predictive and interpretable and understandable for humans. Explainable AI is receiving huge interest in the machine learning and AI research communities, across academia, industry, and government, and there is now an excellent opportunity to push towards successful explainable AI applications. This volume will help the research community to accelerate this process, to promote a more systematic use of explainable AI to improve models in diverse applications, and ultimately to better understand how current explainable AI methods need to be improved and what kind of theory of explainable AI is needed. After overviews of current methods and challenges, the editors include chapters that describe new developments in explainable AI. The contributions are from leading researchers in the field, drawn from both academia and industry, and many of the chapters take a clear interdisciplinary approach to problem-solving. The concepts discussed include explainability, causability, and AI interfaces with humans, and the applications include image processing, natural language, law, fairness, and climate science
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