Author : Charles Sutton
Publisher : Now Pub
Page : 120 pages
File Size : 31,40 MB
Release : 2012
Category : Computers
ISBN : 9781601985729
An Introduction to Conditional Random Fields provides a comprehensive tutorial aimed at application-oriented practitioners seeking to apply CRFs. The monograph does not assume previous knowledge of graphical modeling, and so is intended to be useful to practitioners in a wide variety of fields.
Author : Y.A. Rozanov
Publisher : Springer Science & Business Media
Page : 207 pages
File Size : 19,82 MB
Release : 2012-12-06
Category : Mathematics
ISBN : 1461381908
In this book we study Markov random functions of several variables. What is traditionally meant by the Markov property for a random process (a random function of one time variable) is connected to the concept of the phase state of the process and refers to the independence of the behavior of the process in the future from its behavior in the past, given knowledge of its state at the present moment. Extension to a generalized random process immediately raises nontrivial questions about the definition of a suitable" phase state," so that given the state, future behavior does not depend on past behavior. Attempts to translate the Markov property to random functions of multi-dimensional "time," where the role of "past" and "future" are taken by arbitrary complementary regions in an appro priate multi-dimensional time domain have, until comparatively recently, been carried out only in the framework of isolated examples. How the Markov property should be formulated for generalized random functions of several variables is the principal question in this book. We think that it has been substantially answered by recent results establishing the Markov property for a whole collection of different classes of random functions. These results are interesting for their applications as well as for the theory. In establishing them, we found it useful to introduce a general probability model which we have called a random field. In this book we investigate random fields on continuous time domains. Contents CHAPTER 1 General Facts About Probability Distributions §1.
Author : Alexey Grigorev
Publisher : Packt Publishing Ltd
Page : 310 pages
File Size : 26,67 MB
Release : 2018-03-28
Category : Computers
ISBN : 1788398386
Leverage the power of Tensorflow to design deep learning systems for a variety of real-world scenarios Key Features Build efficient deep learning pipelines using the popular Tensorflow framework Train neural networks such as ConvNets, generative models, and LSTMs Includes projects related to Computer Vision, stock prediction, chatbots and more Book Description TensorFlow is one of the most popular frameworks used for machine learning and, more recently, deep learning. It provides a fast and efficient framework for training different kinds of deep learning models, with very high accuracy. This book is your guide to master deep learning with TensorFlow with the help of 10 real-world projects. TensorFlow Deep Learning Projects starts with setting up the right TensorFlow environment for deep learning. Learn to train different types of deep learning models using TensorFlow, including Convolutional Neural Networks, Recurrent Neural Networks, LSTMs, and Generative Adversarial Networks. While doing so, you will build end-to-end deep learning solutions to tackle different real-world problems in image processing, recommendation systems, stock prediction, and building chatbots, to name a few. You will also develop systems that perform machine translation, and use reinforcement learning techniques to play games. By the end of this book, you will have mastered all the concepts of deep learning and their implementation with TensorFlow, and will be able to build and train your own deep learning models with TensorFlow confidently. What you will learn Set up the TensorFlow environment for deep learning Construct your own ConvNets for effective image processing Use LSTMs for image caption generation Forecast stock prediction accurately with an LSTM architecture Learn what semantic matching is by detecting duplicate Quora questions Set up an AWS instance with TensorFlow to train GANs Train and set up a chatbot to understand and interpret human input Build an AI capable of playing a video game by itself –and win it! Who this book is for This book is for data scientists, machine learning developers as well as deep learning practitioners, who want to build interesting deep learning projects that leverage the power of Tensorflow. Some understanding of machine learning and deep learning, and familiarity with the TensorFlow framework is all you need to get started with this book.
Author : Havard Rue
Publisher : CRC Press
Page : 280 pages
File Size : 18,97 MB
Release : 2005-02-18
Category : Mathematics
ISBN : 0203492021
Gaussian Markov Random Field (GMRF) models are most widely used in spatial statistics - a very active area of research in which few up-to-date reference works are available. This is the first book on the subject that provides a unified framework of GMRFs with particular emphasis on the computational aspects. This book includes extensive case-studie
Author : Stan Z. Li
Publisher : Springer Science & Business Media
Page : 372 pages
File Size : 42,8 MB
Release : 2009-04-03
Category : Computers
ISBN : 1848002793
Markov random field (MRF) theory provides a basis for modeling contextual constraints in visual processing and interpretation. It enables us to develop optimal vision algorithms systematically when used with optimization principles. This book presents a comprehensive study on the use of MRFs for solving computer vision problems. Various vision models are presented in a unified framework, including image restoration and reconstruction, edge and region segmentation, texture, stereo and motion, object matching and recognition, and pose estimation. This third edition includes the most recent advances and has new and expanded sections on topics such as: Bayesian Network; Discriminative Random Fields; Strong Random Fields; Spatial-Temporal Models; Learning MRF for Classification. This book is an excellent reference for researchers working in computer vision, image processing, statistical pattern recognition and applications of MRFs. It is also suitable as a text for advanced courses in these areas.
Author : Kohei Arai
Publisher : Springer Nature
Page : 943 pages
File Size : 17,93 MB
Release : 2020-02-13
Category : Technology & Engineering
ISBN : 3030394425
This book presents high-quality research on the concepts and developments in the field of information and communication technologies, and their applications. It features 134 rigorously selected papers (including 10 poster papers) from the Future of Information and Communication Conference 2020 (FICC 2020), held in San Francisco, USA, from March 5 to 6, 2020, addressing state-of-the-art intelligent methods and techniques for solving real-world problems along with a vision of future research. Discussing various aspects of communication, data science, ambient intelligence, networking, computing, security and Internet of Things, the book offers researchers, scientists, industrial engineers and students valuable insights into the current research and next generation information science and communication technologies.
Author : Yun-Hsuan Sung
Publisher : Stanford University
Page : 161 pages
File Size : 10,11 MB
Release : 2010
Category :
ISBN :
This thesis investigates using a new graphical model, hidden conditional random fields (HCRFs), for speech recognition. Conditional random fields (CRFs) are discriminative sequence models that have been successfully applied to several tasks in text processing, such as named entity recognition. Recently, there has been increasing interest in applying CRFs to speech recognition due to the similarity between speech and text processing. HCRFs are CRFs augmented with hidden variables that are capable of representing the dynamic changes and variations in speech signals. HCRFs also have the ability to incorporate correlated features from both speech signals and text without making strong independence assumptions among them. This thesis presents my current research on applying HCRFs to speech recognition and HCRFs' potential to replace the current hidden Markov model (HMM) for acoustic modeling. Experimental results of phone classification, phone recognition, and speaker adaptation are presented and discussed. Our monophone HCRFs outperform both maximum mutual information estimation (MMIE) and minimum phone error (MPE) trained HMMs and achieve the-start-of-the-art performance in TIMIT phone classification and recognition tasks. We also show how to jointly train acoustic models and language models in HCRFs, which shows improvement in the results. Maximum a posterior (MAP) and maximum conditional likelihood linear regression (MCLLR) successfully adapt speaker-independent models to speaker-dependent models with a small amount of adaptation data for HCRF speaker adaptation. Finally, we explore adding gender and dialect features for phone recognition, and experimental results are presented.
Author : Sebastian Thrun
Publisher : Springer
Page : 582 pages
File Size : 42,94 MB
Release : 2007-05-16
Category : Technology & Engineering
ISBN : 3540481133
This volume contains 50 papers presented at the 12th International Symposium of Robotics Research, which took place October 2005 in San Francisco, CA. Coverage includes: physical human-robot interaction, humanoids, mechanisms and design, simultaneous localization and mapping, field robots, robotic vision, robot design and control, underwater robotics, learning and adaptive behavior, networked robotics, and interfaces and interaction.
Author : Mahdi Bohlouli
Publisher : Springer Nature
Page : 350 pages
File Size : 13,20 MB
Release : 2020-01-28
Category : Technology & Engineering
ISBN : 3030373096
This book presents outstanding theoretical and practical findings in data science and associated interdisciplinary areas. Its main goal is to explore how data science research can revolutionize society and industries in a positive way, drawing on pure research to do so. The topics covered range from pure data science to fake news detection, as well as Internet of Things in the context of Industry 4.0. Data science is a rapidly growing field and, as a profession, incorporates a wide variety of areas, from statistics, mathematics and machine learning, to applied big data analytics. According to Forbes magazine, “Data Science” was listed as LinkedIn’s fastest-growing job in 2017. This book presents selected papers from the International Conference on Contemporary Issues in Data Science (CiDaS 2019), a professional data science event that provided a real workshop (not “listen-shop”) where scientists and scholars had the chance to share ideas, form new collaborations, and brainstorm on major challenges; and where industry experts could catch up on emerging solutions to help solve their concrete data science problems. Given its scope, the book will benefit not only data scientists and scientists from other domains, but also industry experts, policymakers and politicians.
Author : Ross Kindermann
Publisher :
Page : 160 pages
File Size : 50,10 MB
Release : 1980
Category : Mathematics
ISBN :
The study of Markov random fields has brought exciting new problems to probability theory which are being developed in parallel with basic investigation in other disciplines, most notably physics. The mathematical and physical literature is often quite technical. This book aims at a more gentle introduction to these new areas of research.
