Download or read book Bayesian Non parametrics for Multi modal Segmentation written by Wei-Chen Chiu and published by . This book was released on 2016 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Nonparametric Bayesian Learning for Collaborative Robot Multimodal Introspection written by Xuefeng Zhou and published by Springer Nature. This book was released on 2020-01-01 with total page 149 pages. Available in PDF, EPUB and Kindle. Book excerpt: This open access book focuses on robot introspection, which has a direct impact on physical human-robot interaction and long-term autonomy, and which can benefit from autonomous anomaly monitoring and diagnosis, as well as anomaly recovery strategies. In robotics, the ability to reason, solve their own anomalies and proactively enrich owned knowledge is a direct way to improve autonomous behaviors. To this end, the authors start by considering the underlying pattern of multimodal observation during robot manipulation, which can effectively be modeled as a parametric hidden Markov model (HMM). They then adopt a nonparametric Bayesian approach in defining a prior using the hierarchical Dirichlet process (HDP) on the standard HMM parameters, known as the Hierarchical Dirichlet Process Hidden Markov Model (HDP-HMM). The HDP-HMM can examine an HMM with an unbounded number of possible states and allows flexibility in the complexity of the learned model and the development of reliable and scalable variational inference methods. This book is a valuable reference resource for researchers and designers in the field of robot learning and multimodal perception, as well as for senior undergraduate and graduate university students.
Download or read book Bayesian Nonparametrics written by J.K. Ghosh and published by Springer Science & Business Media. This book was released on 2006-05-11 with total page 311 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is the first systematic treatment of Bayesian nonparametric methods and the theory behind them. It will also appeal to statisticians in general. The book is primarily aimed at graduate students and can be used as the text for a graduate course in Bayesian non-parametrics.
Download or read book Bayesian Nonparametric Data Analysis written by Peter Müller and published by Springer. This book was released on 2015-06-17 with total page 203 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book reviews nonparametric Bayesian methods and models that have proven useful in the context of data analysis. Rather than providing an encyclopedic review of probability models, the book’s structure follows a data analysis perspective. As such, the chapters are organized by traditional data analysis problems. In selecting specific nonparametric models, simpler and more traditional models are favored over specialized ones. The discussed methods are illustrated with a wealth of examples, including applications ranging from stylized examples to case studies from recent literature. The book also includes an extensive discussion of computational methods and details on their implementation. R code for many examples is included in online software pages.
Download or read book Nonparametric Bayesian Approaches for Acoustic Modeling written by Amir Hossein Harati Nejad Torbati and published by . This book was released on 2015 with total page 155 pages. Available in PDF, EPUB and Kindle. Book excerpt: The goal of Bayesian analysis is to reduce the uncertainty about unobserved variables by combining prior knowledge with observations. A fundamental limitation of a parametric statistical model, including a Bayesian approach, is the inability of the model to learn new structures. The goal of the learning process is to estimate the correct values for the parameters. The accuracy of these parameters improves with more data but the model's structure remains fixed. Therefore new observations will not affect the overall complexity (e.g. number of parameters in the model). Recently, nonparametric Bayesian methods have become a popular alternative to Bayesian approaches because the model structure is learned simultaneously with the parameter distributions in a data-driven manner. The goal of this dissertation is to apply nonparametric Bayesian approaches to the acoustic modeling problem in continuous speech recognition. Three important problems are addressed: (1) statistical modeling of sub-word acoustic units; (2) semi-supervised training algorithms for nonparametric acoustic models; and (3) automatic discovery of sub-word acoustic units. We have developed a Doubly Hierarchical Dirichlet Process Hidden Markov Model (DHDPHMM) with a non-ergodic structure that can be applied to problems involving sequential modeling. DHDPHMM shares mixture components between states using two Hierarchical Dirichlet Processes (HDP). An inference algorithm for this model has been developed that enables DHDPHMM to outperform both its hidden Markov model (HMM) and HDP HMM (HDPHMM) counterparts. This inference algorithm is shown to also be computationally less expensive than a comparable algorithm for HDPHMM. In addition to sharing data, the proposed model can learn non-ergodic structures and non-emitting states, something that HDPHMM does not support. This extension to the model is used to model finite length sequences. We have also developed a generative model for semi-supervised training of DHDPHMMs. Semi-supervised learning is an important practical requirement for many machine learning applications including acoustic modeling in speech recognition. The relative improvement in error rates on classification and recognition tasks is shown to be 22% and 7% respectively. Semi-supervised training results are slightly better than supervised training (29.02% vs. 29.71%). Context modeling was also investigated and results show a modest improvement of 1.5% relative over the baseline system. We also introduce a nonparametric Bayesian transducer based on an ergodic HDPHMM/DHDPHMM that automatically segments and clusters the speech signal using an unsupervised approach. This transducer was used in several applications including speech segmentation, acoustic unit discovery, spoken term detection and automatic generation of a pronunciation lexicon. For the segmentation problem, an F¬¬¬¬¬¬-score of 76.62% was achieved which represents a 9% relative improvement over the baseline system. On the spoken term detection tasks, an average precision of 64.91% was achieved, which represents a 20% improvement over the baseline system. Lexicon generation experiments also show automatically discovered units (ADU) generalize to new datasets. In this dissertation, we have established the foundation for applications of non-parametric Bayesian modeling to problems such as speech recognition that involve sequential modeling. These models allow a new generation of machine learning systems that adapt their overall complexity in a data-driven manner and yet preserve meaningful modalities in the data. As a result, these models improve generalization and offer higher performance at lower complexity.
Download or read book Bayesian Non parametric Models for Time Segmentation and Regression written by Ava Bargi and published by . This book was released on 2015 with total page 312 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Multimodal Brain Tumor Segmentation and Beyond written by Bjoern Menze and published by Frontiers Media SA. This book was released on 2021-08-10 with total page 324 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Big Data in Multimodal Medical Imaging written by Ayman El-Baz and published by CRC Press. This book was released on 2019-11-05 with total page 247 pages. Available in PDF, EPUB and Kindle. Book excerpt: There is an urgent need to develop and integrate new statistical, mathematical, visualization, and computational models with the ability to analyze Big Data in order to retrieve useful information to aid clinicians in accurately diagnosing and treating patients. The main focus of this book is to review and summarize state-of-the-art big data and deep learning approaches to analyze and integrate multiple data types for the creation of a decision matrix to aid clinicians in the early diagnosis and identification of high risk patients for human diseases and disorders. Leading researchers will contribute original research book chapters analyzing efforts to solve these important problems.
Download or read book Multi Modality State of the Art Medical Image Segmentation and Registration Methodologies written by Ayman S. El-Baz and published by Springer Science & Business Media. This book was released on 2011-04-11 with total page 369 pages. Available in PDF, EPUB and Kindle. Book excerpt: With the advances in image guided surgery for cancer treatment, the role of image segmentation and registration has become very critical. The central engine of any image guided surgery product is its ability to quantify the organ or segment the organ whether it is a magnetic resonance imaging (MRI) and computed tomography (CT), X-ray, PET, SPECT, Ultrasound, and Molecular imaging modality. Sophisticated segmentation algorithms can help the physicians delineate better the anatomical structures present in the input images, enhance the accuracy of medical diagnosis and facilitate the best treatment planning system designs. The focus of this book in towards the state of the art techniques in the area of image segmentation and registration.
Download or read book Bayesian Nonparametric Methods for Dynamics Identification and Segmentation for Powered Prosthesis Control written by Neil Dhir and published by . This book was released on 2017 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Bayesian Nonparametrics written by Nils Lid Hjort and published by Cambridge University Press. This book was released on 2010-04-12 with total page 309 pages. Available in PDF, EPUB and Kindle. Book excerpt: Bayesian nonparametrics works - theoretically, computationally. The theory provides highly flexible models whose complexity grows appropriately with the amount of data. Computational issues, though challenging, are no longer intractable. All that is needed is an entry point: this intelligent book is the perfect guide to what can seem a forbidding landscape. Tutorial chapters by Ghosal, Lijoi and Prünster, Teh and Jordan, and Dunson advance from theory, to basic models and hierarchical modeling, to applications and implementation, particularly in computer science and biostatistics. These are complemented by companion chapters by the editors and Griffin and Quintana, providing additional models, examining computational issues, identifying future growth areas, and giving links to related topics. This coherent text gives ready access both to underlying principles and to state-of-the-art practice. Specific examples are drawn from information retrieval, NLP, machine vision, computational biology, biostatistics, and bioinformatics.
Download or read book Bayesian Nonparametrics and Marked Poisson Processes written by Sindhu Ghanta and published by . This book was released on 2014 with total page 142 pages. Available in PDF, EPUB and Kindle. Book excerpt: Motivated by problems in image processing involving segmentation and detection of multiple instances of complex objects, this dissertation explores the use of marked Poisson point processes within a Bayesian nonparametric framework. The Poisson point process underlies a wide range of combinatorial stochastic processes and as such has been a key object driving research in Bayesian nonparametrics. We explore Poisson point processes in combination with probabilistic shape and appearance priors for detection/segmentation of objects/patterns in 1D, 2D and 3D frameworks. This probabilistic formulation encompasses uncertainty in number, location, shape, and appearance of the feature of interest, be it in images or time-series data. The generative process of the model can be explained as sampling a random number of objects at random locations from a Poisson process. The shape of each object is sampled from a shape model. The appearance inside and outside the shape boundary is sampled from an appearance model with foreground and background parameters respectively. The Poisson intensity parameter can either be homogeneous (uniform) or non-homogeneous. A non-homogeneous Poisson prior provides the flexibility to incorporate spatial context information regarding where the high or low concentration areas occur. We model the non-homogeneous Poisson intensity with a log-Gaussian Cox process. For shape, any probabilistic model can be used. We describe examples of both, parametric and complex shape priors. Appearance features can be simple intensity values of the image or higher level features such as texture. Inference on the proposed model is complicated due to changing model order, use of non-conjugate priors, and a likelihood that depends on partitioning based on shape boundaries. Inference on such models typically involves a reversible-jump Markov chain Monte Carlo (RJMCMC). We introduce a simpler Gibbs sampling approach which can be accomplished by leveraging the discrete nature of digital images. We demonstrate empirical results on 2D images. We also generalize and extend our model with Gibbs sampling on 1D and 3D data. We show case studies of our method on a diverse set of images: cell image segmentation, traffic surveillance, and 3D segmentation of the dermal-epidermal junction of reflectance confocal microscopy images of human skin to aid in cancer detection. We also present the work done in the Versatile Onboard Traffic Embedded Roaming Sensors (VOTERS) project as a part of this dissertation. VOTERS aims to detect pavement quality using the data captured by several sensors mounted on a vehicle. The goal is to design a non-invasive technique that can assess the pavement quality without disrupting regular traffic. We have developed algorithms to detect surface defects (for e.g. cracks, along with their type, length, width, and area covered) from video data. These features form a key component in the determination of pavement condition by Civil Engineers. Data is acquired from the video camera using a software trigger developed to capture images at regular intervals of distance rather than time resulting in efficient use of hard-disk space. We present a quantified and thorough analysis using groundtruth data that will be made publicly available.
Download or read book Multimodal Analysis of User Generated Multimedia Content written by Rajiv Shah and published by Springer. This book was released on 2017-08-30 with total page 279 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents a summary of the multimodal analysis of user-generated multimedia content (UGC). Several multimedia systems and their proposed frameworks are also discussed. First, improved tag recommendation and ranking systems for social media photos, leveraging both content and contextual information, are presented. Next, we discuss the challenges in determining semantics and sentics information from UGC to obtain multimedia summaries. Subsequently, we present a personalized music video generation system for outdoor user-generated videos. Finally, we discuss approaches for multimodal lecture video segmentation techniques. This book also explores the extension of these multimedia system with the use of heterogeneous continuous streams.
Download or read book Bayesian Nonparametric Methods for Non exchangeable Data written by Nicholas J. Foti and published by . This book was released on 2013 with total page 358 pages. Available in PDF, EPUB and Kindle. Book excerpt: Bayesian nonparametric methods have become increasingly popular in machine learning for their ability to allow the data to determine model complexity. In particular, Bayesian nonparametric versions of common latent variable models can learn as effective dimension of the latent space. Examples include mixture models, latent feature models and topic models, where the number of components, features, or topics need not be specified a priori. A drawback of many of these models is that they assume the observations are exchangeable, that is, any dependencies between observations are ignored. This thesis contributes general methods to incorporate covariates into Bayesian nonparametric models and inference algorithms to learn with these models. First, we will present a flexible class of dependent Bayesian nonparametric priors to induce covariate-dependence into a variety of latent variable models used in machine learning. The proposed framework has nice analytic properites and admits a simple inference algorithm. We show how the framework can be used to construct a covariate-dependent latent feature model and a time-varying topic model. Second, we describe the first general purpose inference algorithm for a large family of dependent mixture models. Using the idea of slice-sampling, the algorithm is truncation-free and fast, showing that inference can de done efficiently despite the added complexity that covariate-dependence entails. Last, we construct a Bayesian nonparametric framework to couple multiple latent variable models and apply the framework to learning from multiple views of data.
Download or read book The Oxford Handbook of Computational and Mathematical Psychology written by Jerome R. Busemeyer and published by Oxford University Press. This book was released on 2015-03-20 with total page 425 pages. Available in PDF, EPUB and Kindle. Book excerpt: This Oxford Handbook offers a comprehensive and authoritative review of important developments in computational and mathematical psychology. With chapters written by leading scientists across a variety of subdisciplines, it examines the field's influence on related research areas such as cognitive psychology, developmental psychology, clinical psychology, and neuroscience. The Handbook emphasizes examples and applications of the latest research, and will appeal to readers possessing various levels of modeling experience. The Oxford Handbook of Computational and mathematical Psychology covers the key developments in elementary cognitive mechanisms (signal detection, information processing, reinforcement learning), basic cognitive skills (perceptual judgment, categorization, episodic memory), higher-level cognition (Bayesian cognition, decision making, semantic memory, shape perception), modeling tools (Bayesian estimation and other new model comparison methods), and emerging new directions in computation and mathematical psychology (neurocognitive modeling, applications to clinical psychology, quantum cognition). The Handbook would make an ideal graduate-level textbook for courses in computational and mathematical psychology. Readers ranging from advanced undergraduates to experienced faculty members and researchers in virtually any area of psychology--including cognitive science and related social and behavioral sciences such as consumer behavior and communication--will find the text useful.
Download or read book A Bayesian Non Parametric Dynamic AR Model for Multiple Time Series Analysis written by Luis E. Nieto-Barajas and published by . This book was released on 2016 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this article, we propose a Bayesian non-parametric model for the analysis of multiple time series. We consider an autoregressive structure of order p for each of the series and borrow strength across the series by considering a common error population that is also evolving in time. The error populations (distributions) are assumed non-parametric whose law is based on a series of dependent Polya trees with zero median. This dependence is of order q and is achieved via a dependent beta process that links the branching probabilities of the trees. We study the prior properties and show how to obtain posterior inference. The model is tested under a simulation study and is illustrated with the analysis of the economic activity index of the 32 states of Mexico.
Download or read book Non parametric Bayesian Methods for Structured Topic Models written by Lan Du and published by . This book was released on 2011 with total page 332 pages. Available in PDF, EPUB and Kindle. Book excerpt: The proliferation of large electronic document archives requires new techniques for automatically analysing large collections, which has posed several new and interesting research challenges. Topic modelling, as a promising statistical technique, has gained significant momentum in recent years in information retrieval, sentiment analysis, images processing, etc. Besides existing topic models, the field of topic modelling still needs to be further explored using more powerful tools. One potentially useful area is to directly consider the document structure ranging from semantically high-level segments (e.g., chapters, sections, or paragraphs) to low-level segments (e.g., sentences or words) in topic modeling. This thesis introduces a family of structured topic models for statistically modeling text documents together with their intrinsic document structures. These models take advantage of non-parametric Bayesian techniques (e.g., the two-parameter Poisson-Dirichlet process (PDP)) and Markov chain Monte Carlo methods. Two preliminary contributions of this thesis are 1. The Compound Poisson-Dirichlet process (CPDP): it is an extension of the PDP that can be applied to multiple input distributions. 2. Two Gibbs sampling algorithms for the PDP in a finite state space: these two samplers are based on the Chinese restaurant process that provides an elegant analogy of incremental sampling for the PDP. The first, a two-stage Gibbs sampler, arises from a table multiplicity representation for the PDP. The second is built on top of a table indicator representation. In a simply controlled environment of multinomial sampling, the two new samplers have fast convergence speed. These support the major contribution of this thesis, which is a set of structured topic models: Segmented Topic Model (STM) which models a simple document structure with a four-level hierarchy by mapping the document layout to a hierarchical subject structure. It performs significantly better than the latent Dirichlet allocation model and other segmented models at predicting unseen words. Sequential Latent Dirichlet Allocation (SeqLDA) which is motivated by topical correlations among adjacent segments (i.e., the sequential document structure). This new model uses the PDP and a simple first-order Markov chain to link a set of LDAs together. It provides a novel approach for exploring the topic evolution within each individual document. Adaptive Topic Model (AdaTM) which embeds the CPDP in a simple directed acyclic graph to jointly model both hierarchical and sequential document structures. This new model demonstrates in terms of per-word predictive accuracy and topic distribution profile analysis that it is beneficial to consider both forms of structures in topic modelling. - provided by Candidate.
