Introduction To Semi Supervised Learning

Introduction to Semi supervised Learning PDF
Author: Xiaojin Zhu
Publisher: Morgan & Claypool Publishers
ISBN: 1598295470
Size: 66.77 MB
Format: PDF, ePub, Docs
Category : Computers
Languages : en
Pages : 116
View: 5547

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Book Description: Semi-supervised learning is a learning paradigm concerned with the study of how computers and natural systems such as humans learn in the presence of both labeled and unlabeled data. Traditionally, learning has been studied either in the unsupervised paradigm (e.g., clustering, outlier detection) where all the data are unlabeled, or in the supervised paradigm (e.g., classification, regression) where all the data are labeled. The goal of semi-supervised learning is to understand how combining labeled and unlabeled data may change the learning behavior, and design algorithms that take advantage of such a combination. Semi-supervised learning is of great interest in machine learning and data mining because it can use readily available unlabeled data to improve supervised learning tasks when the labeled data are scarce or expensive. Semi-supervised learning also shows potential as a quantitative tool to understand human category learning, where most of the input is self-evidently unlabeled. In this introductory book, we present some popular semi-supervised learning models, including self-training, mixture models, co-training and multiview learning, graph-based methods, and semi-supervised support vector machines. For each model, we discuss its basic mathematical formulation. The success of semi-supervised learning depends critically on some underlying assumptions. We emphasize the assumptions made by each model and give counterexamples when appropriate to demonstrate the limitations of the different models. In addition, we discuss semi-supervised learning for cognitive psychology. Finally, we give a computational learning theoretic perspective on semi-supervised learning, and we conclude the book with a brief discussion of open questions in the field. Table of Contents: Introduction to Statistical Machine Learning / Overview of Semi-Supervised Learning / Mixture Models and EM / Co-Training / Graph-Based Semi-Supervised Learning / Semi-Supervised Support Vector Machines / Human Semi-Supervised Learning / Theory and Outlook

Introduction To Natural Language Processing

Introduction to Natural Language Processing PDF
Author: Jacob Eisenstein
Publisher: MIT Press
ISBN: 0262042843
Size: 76.49 MB
Format: PDF, ePub, Docs
Category : Computers
Languages : en
Pages : 536
View: 5625

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Book Description: A survey of computational methods for understanding, generating, and manipulating human language, which offers a synthesis of classical representations and algorithms with contemporary machine learning techniques. This textbook provides a technical perspective on natural language processing—methods for building computer software that understands, generates, and manipulates human language. It emphasizes contemporary data-driven approaches, focusing on techniques from supervised and unsupervised machine learning. The first section establishes a foundation in machine learning by building a set of tools that will be used throughout the book and applying them to word-based textual analysis. The second section introduces structured representations of language, including sequences, trees, and graphs. The third section explores different approaches to the representation and analysis of linguistic meaning, ranging from formal logic to neural word embeddings. The final section offers chapter-length treatments of three transformative applications of natural language processing: information extraction, machine translation, and text generation. End-of-chapter exercises include both paper-and-pencil analysis and software implementation. The text synthesizes and distills a broad and diverse research literature, linking contemporary machine learning techniques with the field's linguistic and computational foundations. It is suitable for use in advanced undergraduate and graduate-level courses and as a reference for software engineers and data scientists. Readers should have a background in computer programming and college-level mathematics. After mastering the material presented, students will have the technical skill to build and analyze novel natural language processing systems and to understand the latest research in the field.

Managing And Understanding Artificial Intelligence Solutions

Managing and Understanding Artificial Intelligence Solutions PDF
Author: Hildesheim Wolfgang
Publisher: Beuth Verlag GmbH
ISBN: 341030407X
Size: 62.52 MB
Format: PDF, Kindle
Category : Technology & Engineering
Languages : en
Pages : 64
View: 1894

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Book Description: KI ist ein weltweiter Megatrend. Bedeutung, Leistung und Komplexität von KI-Lösungen nehmen rasant zu und daher wächst auch der Bedarf, einen Überblick über die relevanten KI-Lösungen zu behalten und die damit verbundenen Prioritäten und Risiken zu managen. Das vorgestellte AI Methods, Capabilities and Criticality Grid (AI-MC2-Grid) stellt eine Methode und ein Werkzeug dar, um diesen Überblick zu gewinnen und die KI-Lösungen zu verwalten. Nutzer des AI-MC2-Grid können Manager, Entwickler und Anwender von KI-Lösungen sein, aber auch Investoren, Politiker und Regelsetzer, die den Markt verstehen und bestimmte KI-Lösungen verwalten wollen.Das AI-MC2-Grid besteht aus drei Dimensionen: KI-Methoden, KI-Fähigkeiten und die Kritikalität einer KI-Lösung. Jede diskutierte KI-Lösung kann in diese drei Dimensionen eingeordnet werden, so dass ähnliche KI-Lösungen verglichen werden können. Alternativ können komplexe KI-Lösungen anhand ihrer Komponenten analysiert werden. KI-Methoden entsprechen dabei typischen KI-Algorithmen, während KI-Fähigkeiten typischen Prozessschritten zum Aufbau intelligenter Workflows beschreiben. Sind die relevanten KI-Methoden und KI-Fähigkeiten einer bestimmten KI-Lösung gefunden, können Leistung, Folgen und mögliche Risiken und Alternativen diskutiert werden. Basierend auf der Klassifizierung stellt das Schadenspotenzial von Künstlicher Intelligenz eine bestimmte Stufe der Kritikalität dar. In diesem Zuge steigen mit zunehmender Kritikalität auch die Anforderungen an Tests, Kalibrierung, Inspektion, Kontrolle und Zertifizierung. Das AI-MC2-Grid eine leistungsfähige Methode und ein Werkzeug, um alle Arten von kommenden Normen und Standards von KI-Lösungen zu definieren und zu verwalten.Aus diesem guten Grund steht das AI-MC2-Grid im Mittelpunkt der Deutschen Normungsroadmap für Künstlichen Intelligenz, die als Werkzeug zur Unterstützung der Entwicklung und des Managements zukünftiger KI-Standards und -Normen

Introduction To Deep Learning

Introduction to Deep Learning PDF
Author: Eugene Charniak
Publisher:
ISBN: 0262039516
Size: 50.57 MB
Format: PDF, ePub, Mobi
Category : Computers
Languages : en
Pages : 174
View: 3317

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Book Description: A project-based guide to the basics of deep learning.

Semi Supervised Learning And Domain Adaptation In Natural Language Processing

Semi Supervised Learning and Domain Adaptation in Natural Language Processing PDF
Author: Anders Søgaard
Publisher: Morgan & Claypool Publishers
ISBN: 1608459861
Size: 48.18 MB
Format: PDF, Docs
Category : Computers
Languages : en
Pages : 103
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Book Description: This book introduces basic supervised learning algorithms applicable to natural language processing (NLP) and shows how the performance of these algorithms can often be improved by exploiting the marginal distribution of large amounts of unlabeled data. One reason for that is data sparsity, i.e., the limited amounts of data we have available in NLP. However, in most real-world NLP applications our labeled data is also heavily biased. This book introduces extensions of supervised learning algorithms to cope with data sparsity and different kinds of sampling bias. This book is intended to be both readable by first-year students and interesting to the expert audience. My intention was to introduce what is necessary to appreciate the major challenges we face in contemporary NLP related to data sparsity and sampling bias, without wasting too much time on details about supervised learning algorithms or particular NLP applications. I use text classification, part-of-speech tagging, and dependency parsing as running examples, and limit myself to a small set of cardinal learning algorithms. I have worried less about theoretical guarantees ("this algorithm never does too badly") than about useful rules of thumb ("in this case this algorithm may perform really well"). In NLP, data is so noisy, biased, and non-stationary that few theoretical guarantees can be established and we are typically left with our gut feelings and a catalogue of crazy ideas. I hope this book will provide its readers with both. Throughout the book we include snippets of Python code and empirical evaluations, when relevant.

Enhancement To Selective Incremental Approach For Transductive Nearest Neighbour Classification

Enhancement to Selective Incremental Approach for Transductive Nearest Neighbour Classification PDF
Author: E. Madhusudhana Reddy
Publisher: GRIN Verlag
ISBN: 3656341869
Size: 18.51 MB
Format: PDF, ePub, Docs
Category : Computers
Languages : en
Pages : 133
View: 897

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Book Description: Master's Thesis from the year 2012 in the subject Computer Science - Didactics, , course: COMPUTER SCIENCE & ENGINEERING, language: English, abstract: During the last years, semi-supervised learning has emerged as an exciting new direction in machine learning research. It is closely related to profound issues of how to do inference from data, as witnessed by its overlap with transductive inference. Semi-Supervised learning is the half-way between Supervised and Unsupervised Learning. In this majority of the patterns are unlabelled, they are present in Test set and knowed labeled patterns are present in Training set. Using these training set, we assign the labels for test set. Here our Proposed method is using Nearest Neighbour Classifier for Semi-Supervised learning we can label the unlabelled patterns using the labeled patterns and then compare these method with the traditionally Existing methods as graph mincut, spectral graph partisan, ID3,Nearest Neighbour Classifier and we are going to prove our Proposed method is more scalable than the Existing methods and reduce time complexity of SITNNC(Selective Incremental Approach for Transductive Nearest Neighbour Classifier) using Leaders Algorithm.

Graph Based Semi Supervised Learning

Graph Based Semi Supervised Learning PDF
Author: Amarnag Subramanya
Publisher: Morgan & Claypool Publishers
ISBN: 162705202X
Size: 18.29 MB
Format: PDF, Mobi
Category : Computers
Languages : en
Pages : 125
View: 3890

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Book Description: While labeled data is expensive to prepare, ever increasing amounts of unlabeled data is becoming widely available. In order to adapt to this phenomenon, several semi-supervised learning (SSL) algorithms, which learn from labeled as well as unlabeled data, have been developed. In a separate line of work, researchers have started to realize that graphs provide a natural way to represent data in a variety of domains. Graph-based SSL algorithms, which bring together these two lines of work, have been shown to outperform the state-of-the-art in many applications in speech processing, computer vision, natural language processing, and other areas of Artificial Intelligence. Recognizing this promising and emerging area of research, this synthesis lecture focuses on graph-based SSL algorithms (e.g., label propagation methods). Our hope is that after reading this book, the reader will walk away with the following: (1) an in-depth knowledge of the current state-of-the-art in graph-based SSL algorithms, and the ability to implement them; (2) the ability to decide on the suitability of graph-based SSL methods for a problem; and (3) familiarity with different applications where graph-based SSL methods have been successfully applied. Table of Contents: Introduction / Graph Construction / Learning and Inference / Scalability / Applications / Future Work / Bibliography / Authors' Biographies / Index

Bootstrapping Named Entity Annotation By Means Of Active Machine Learning

Bootstrapping Named Entity Annotation by Means of Active Machine Learning PDF
Author: Fredrik Olsson
Publisher:
ISBN:
Size: 78.72 MB
Format: PDF
Category : Computational linguistics
Languages : en
Pages : 220
View: 7692

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Book Description: On the development of a method called BootMark for bootstrapping the marking up of named entities in textual documents.

Supervised Learning With Python

Supervised Learning with Python PDF
Author: Vaibhav Verdhan
Publisher: Apress
ISBN: 9781484261552
Size: 28.73 MB
Format: PDF, Mobi
Category : Computers
Languages : en
Pages : 372
View: 3787

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Book Description: Gain a thorough understanding of supervised learning algorithms by developing use cases with Python. You will study supervised learning concepts, Python code, datasets, best practices, resolution of common issues and pitfalls, and practical knowledge of implementing algorithms for structured as well as text and images datasets. You’ll start with an introduction to machine learning, highlighting the differences between supervised, semi-supervised and unsupervised learning. In the following chapters you’ll study regression and classification problems, mathematics behind them, algorithms like Linear Regression, Logistic Regression, Decision Tree, KNN, Naïve Bayes, and advanced algorithms like Random Forest, SVM, Gradient Boosting and Neural Networks. Python implementation is provided for all the algorithms. You’ll conclude with an end-to-end model development process including deployment and maintenance of the model. After reading Supervised Learning with Python you’ll have a broad understanding of supervised learning and its practical implementation, and be able to run the code and extend it in an innovative manner. What You'll Learn Review the fundamental building blocks and concepts of supervised learning using Python Develop supervised learning solutions for structured data as well as text and images Solve issues around overfitting, feature engineering, data cleansing, and cross-validation for building best fit models Understand the end-to-end model cycle from business problem definition to model deployment and model maintenance Avoid the common pitfalls and adhere to best practices while creating a supervised learning model using Python Who This Book Is For Data scientists or data analysts interested in best practices and standards for supervised learning, and using classification algorithms and regression techniques to develop predictive models.

Introduction To Machine Learning

Introduction to Machine Learning PDF
Author: Ethem Alpaydin
Publisher: MIT Press
ISBN: 0262028182
Size: 43.83 MB
Format: PDF, ePub, Docs
Category : Computers
Languages : en
Pages : 640
View: 5245

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Book Description: The goal of machine learning is to program computers to use example data or past experience to solve a given problem. Many successful applications of machine learning exist already, including systems that analyze past sales data to predict customer behavior, optimize robot behavior so that a task can be completed using minimum resources, and extract knowledge from bioinformatics data. Introduction to Machine Learning is a comprehensive textbook on the subject, covering a broad array of topics not usually included in introductory machine learning texts. Subjects include supervised learning; Bayesian decision theory; parametric, semi-parametric, and nonparametric methods; multivariate analysis; hidden Markov models; reinforcement learning; kernel machines; graphical models; Bayesian estimation; and statistical testing.Machine learning is rapidly becoming a skill that computer science students must master before graduation. The third edition of Introduction to Machine Learning reflects this shift, with added support for beginners, including selected solutions for exercises and additional example data sets (with code available online). Other substantial changes include discussions of outlier detection; ranking algorithms for perceptrons and support vector machines; matrix decomposition and spectral methods; distance estimation; new kernel algorithms; deep learning in multilayered perceptrons; and the nonparametric approach to Bayesian methods. All learning algorithms are explained so that students can easily move from the equations in the book to a computer program. The book can be used by both advanced undergraduates and graduate students. It will also be of interest to professionals who are concerned with the application of machine learning methods.