nonparametric bayesian machine transliteration with synchronous adaptor grammars

... Trang 1M E T H O D O L O G Y A R T I C L E Open AccessA nonparametric Bayesian method of translating machine learning scores to probabilities in clinical decision support Brian ... the method to (non-ML) clinical scores is also discussed Keywords: Statistics, Nonparametric, Bayesian, Calibration, Machine learning Background Clinical decision support systems can be defined ... distribu-tion with zero mean and unit variance, and 50 negative cases are randomly generated from a second Gaussian dis-tribution with a unit variance and certain fractional over-lap with the first

Ngày tải lên: 25/11/2020, 17:05

... statistical machine translation Machine Translation, pages 187-207 Nicola Ueffing, Gholamreza Haffari and Anoop Sarkar. 2008 Semi-supervised Model Adaptation for Statisti-cal Machine Translation Machine ... Learning to Translate with Source and Target Syntax In Proc of ACL 2010, pages 1443-1452. Jorge Civera and Alfons Juan 2007 Domain Adaptation in Statistical Machine Translation with Mixture Mod-elling ... scores under the condition within the range [25K, 80K] are all higher than the ones within the range [5K, 20K] When N is set to 55K, the BLEU score of our system is 21.40, with 1.18 gains on the

Ngày tải lên: 19/02/2014, 19:20

... Trang 1Enhancing Language Models in Statistical Machine Translationwith Backward N-grams and Mutual Information Triggers Deyi Xiong, Min Zhang, Haizhou Li ... paper, with a belief that a language model that embraces a larger context provides better prediction ability, we present two ex-tensions to standard n-gram language mod-els in statistical machine ... syntactically-informed language models are consistent with syntax-based translation models in capturing long-distance dependencies In this paper, we pursue the second direction with-out resorting to any linguistic

Ngày tải lên: 07/03/2014, 22:20

... as a special kind of PCFG, so Bayesian in- ference for PCFGs can be used to infer Topic Models as well. Adaptor Grammars (AGs) are a hierarchical, non-parameteric Bayesian extension of PCFGs. ... Doc j,i → Doc j,i Topic i , Bayesian inference will be biased to find distributions in which adja- cent words will tend to have the same topic. 5 Adaptor Grammars Non-parametric Bayesian inference, where ... properties they have. For example, with the adaptors used here G X typically concentrates mass on a smaller subset of the trees T X than H X does. Just as with the PCFG, an adaptor grammar gen- erates

Ngày tải lên: 23/03/2014, 16:20

... that can end with a dangling morpheme, which can con-nect with other morphemes as part of the trans-lation process, and 2) the use of a fully seg-mented translation model in combination with a post-processing ... Conference on Machine Learning, pages 282–289, San Francisco, California, USA Associ-ation for Computing Machinery. Minh-Thang Luong, Preslav Nakov, and Min-Yen repre-sentation for machine translation ... source-target asymmetry, and prob-lems with automatic evaluation In this work, we propose to address the prob-lem of morphological complexity in an English-to-Finnish MT task within a phrase-based trans-lation

Ngày tải lên: 30/03/2014, 21:20

... Projection matrix Projection matrix Bayesian CS procedure Bayesian CS procedure Bayesian CS procedure + + + Figure 2: Block diagram of decentralized turbo Bayesian compressed sensing Table 1: ... =Φisik+ ik, (1) with k = {1, 2, , K } and i = {1, 2, , I }; the matrix Φi, (Φi ∈ R M × N) is the projection matrix with M  N. The  ik are additive white Gaussian noise with unknown but ... into the current signal estimation 3.1 Bayesian Compressed Sensing Framework Starting with Gaussian distributed noise, the BCS framework [5, 21] builds a Bayesian regression approach to reconstruct

Ngày tải lên: 21/06/2014, 05:20

... Starting with a small value forρ and iterating to convergence, the procedure must be repeated with larger values ofρ until a value is found which sufficiently sharpens the image at convergence and without ... nonnegativity and flux conservation The paper discusses the fundamental idea of the Bayesian image deblurring with the use of entropy as prior, and the analytical analysis of superresolution and ... (a) methods based on regularization [2 4] and (b) methods based on Bayesian theory [2,4,5] The main idea of regularization and Bayesian approach is the use of a priori information expressed by

Ngày tải lên: 21/06/2014, 22:20

... localization. These positioning problems were solved within the Bayesian filtering framework. Filter derivation and implementation algorithms are provided with emphasis on the mapping approach. The radio ... are sum- marized in Ta ble 3. 3. BAYESIAN FILTERING FOR MOBILE TERMINAL POSITIONING 3.1. Foundations of the Bayesian filter and basic algorithm The recursive Bayesian filter (RBF) [17] is a probabilistic ... Khalaf-Allah and K Kyamakya, Bayesian mobile location in cellular networks,” in Proceedings of the... three positioning problems have been suggested within the Bayesian filtering framework

Ngày tải lên: 22/06/2014, 19:20

... transformation 108 Trang 11Naive Bayes with scikit-learn 120Chapter 8: Nonlinear Classification and Regression with Decision Chapter 11: From the Perceptron to Support Vector Machines 164 Maximum margin ... been conceived, and hint at machine learning's potential In this book, we will examine several machine learning models and learning algorithms Wewill discuss tasks that machine learning is commonly ... performance of machine learning systems We will work with a popular libraryfor the Python programming language called scikit-learn, which has assembled state-of-the-art implementations of many machine

Ngày tải lên: 16/08/2018, 15:45

... transformation 108 Trang 11Naive Bayes with scikit-learn 120Chapter 8: Nonlinear Classification and Regression with Decision Chapter 11: From the Perceptron to Support Vector Machines 164 Maximum margin ... been conceived, and hint at machine learning's potential In this book, we will examine several machine learning models and learning algorithms Wewill discuss tasks that machine learning is commonly ... performance of machine learning systems We will work with a popular libraryfor the Python programming language called scikit-learn, which has assembled state-of-the-art implementations of many machine

Ngày tải lên: 24/10/2018, 08:15

... 1: Getting Started with Breeze 1Introduction 1Getting Breeze – the linear algebra library 2 Vectors and matrices with randomly distributed values 25 Chapter 2: Getting Started with Apache Spark ... following recipes: f Getting Breeze—the linear algebra library f Working with vectors f Working with matrices f Vectors and matrices with randomly distributed values f Reading and writing CSV files Introduction ... number crunching with the best performance, we should add the breeze-natives dependency too Trang 21The org.scalanlp.breeze-natives packageWith its native additive, Breeze looks for the machine-specific

Ngày tải lên: 02/03/2019, 11:17

... the Right Software with Machine Learning 7 What Exactly Is Machine Learning? 7 The High Interest Credit Card Debt of Machine Learning 8 SOLID Applied to Machine Learning 9 Machine Learning ... further with machine learning What Exactly Is Machine Learning? According to most definitions, machine learning is a collection of algorithms, techni‐ques, and tricks of the trade that allow machines ... Trang 1Matthew KirkThoughtful Machine Learning with Python A TEST-DRIVEN APPROACH Trang 3Matthew KirkThoughtful Machine Learning with Python A Test-Driven Approach Boston Farnham

Ngày tải lên: 04/03/2019, 11:10

... Trang 1Machine Learning with R Trang 3Abhijit GhatakMachine Learning with R 123 Trang 4Consultant Data EngineerKolkata India ISBN 978-981-10-6807-2 ... through machine learning What is Machine Learning? This book is about data mining and machine learning which helps us to discoverpreviously unknown patterns and relationships in data Machine ... rules,decision trees, and machine learning (including deep learning) • Machine Learning is the subset of AI that includes abstruse statistical niques that enable machines to improve at tasks with the experience

Ngày tải lên: 04/03/2019, 14:56

... 2Mastering Machine Learning with scikit-learnApply effective learning algorithms to real-world problems using scikit-learn Gavin Hackeling BIRMINGHAM - MUMBAI Trang 3Mastering Machine Learning with ... From the Perceptron to Support Vector Machines, discusses efficient nonlinear classification and regression with support vector machines We use support vector machines to recognize the characters ... training sets with millions or billions of examples While this book will not work with datasets that require parallel processing on tens or hundreds of machines, the predictive power of many machine

Ngày tải lên: 12/03/2019, 15:49

... training sets with millions or billions of examples While this book will not work with datasets that require parallel processing on tens or hundreds of machines, the predictive power of many machine ... model with high variance, conversely, will produce different errors for an input depending on the training set that it was trained with A model with high bias is inflexible, but a model with high ... 13Approximating XOR with Multilayer perceptrons 212Summary 214 Index 217 Trang 14Recent years have seen the rise of machine learning, the study of software that learns from experience While machine learning

Ngày tải lên: 12/04/2019, 00:41

... the Right Software with Machine Learning 7 What Exactly Is Machine Learning? 7 The High Interest Credit Card Debt of Machine Learning 8 SOLID Applied to Machine Learning 9 Machine Learning ... further with machine learning What Exactly Is Machine Learning? According to most definitions, machine learning is a collection of algorithms, techni‐ques, and tricks of the trade that allow machines ... Trang 1Matthew KirkThoughtful Machine Learning with Python A TEST-DRIVEN APPROACH www.allitebooks.com Trang 3Matthew KirkThoughtful Machine Learning with Python A Test-Driven Approach Boston

Ngày tải lên: 12/04/2019, 00:47

... training sets with millions or billions of examples While this book will not work with datasets that require parallel processing on tens or hundreds of machines, the predictive power of many machine ... model with high variance, conversely, will produce different errors for an input depending on the training set that it was trained with A model with high bias is inflexible, but a model with high ... 13Approximating XOR with Multilayer perceptrons 212Summary 214 Index 217 Trang 14Recent years have seen the rise of machine learning, the study of software that learns from experience While machine learning

Ngày tải lên: 13/04/2019, 00:19

... the Right Software with Machine Learning 7 What Exactly Is Machine Learning? 7 The High Interest Credit Card Debt of Machine Learning 8 SOLID Applied to Machine Learning 9 Machine Learning ... further with machine learning What Exactly Is Machine Learning? According to most definitions, machine learning is a collection of algorithms, techni‐ques, and tricks of the trade that allow machines ... Trang 1Matthew KirkThoughtful Machine Learning with Python A TEST-DRIVEN APPROACH www.allitebooks.com Trang 3Matthew KirkThoughtful Machine Learning with Python A Test-Driven Approach Boston

Ngày tải lên: 13/04/2019, 00:25

... Trang 3Machine Learning Mastery With Python © Copyright 2016 Jason Brownlee All Rights Reserved Edition: v1.4 Trang 41.1 Learn Python Machine Learning The Wrong Way 2 1.2 Machine Learning ... 12.3 Nonlinear Machine Learning Algorithms 79 12.4 Summary 82 13 Compare Machine Learning Algorithms 83 13.1 Choose The Best Machine Learning Model 83 13.2 Compare Machine Learning ... Modeling Project Template 106 18.1 Practice Machine Learning With Projects 106 Trang 718.2 Machine Learning Project Template in Python 107 18.3 Machine Learning Project Template Steps

Ngày tải lên: 13/04/2019, 01:27

... Trang 7Advanced Machine Learning with PythonTrang 8All rights reserved No part of this book may be reproduced, stored in a retrieval system, or transmitted inany form or by any means, without the prior written permission of the publisher, except in the case of briefquotations embedded in critical articles or reviews ... 38In the previous section, you learned that unsupervised machine learning algorithms are used to extract keystructural or information content from large, possibly complex datasets These algorithms do so with little or no manual input and function without the need for training data (sets of labeled explanatory and ... Having started out in mobile gaming, he was drawn to the challenge of AAAconsole analytics Keen to start putting advanced machine learning techniques into practice, he signed on with Microsoft todevelop player modelling capabilities and big data infrastructure at an Xbox studio

Ngày tải lên: 16/11/2019, 20:54