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Techniques for Exploiting Unlabeled Data

techniques to deal with this information explosion becomes highly significant. One such family of techniques are those which aim to exploit unlabeled;

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Introduction to Semi-Supervised Learning

presence of both labeled and unlabeled data. Traditionally, learning has been studied either in the unsupervised paradigm (e.g., clustering;

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Semi-Supervised Dependency Parsing

in recent years, one of the main reasons for their success is that they can make use of large unlabeled data together with relatively small labeled;

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Learning Ontology Relations by Combining Corpus-Based Techniques and Reasoning on Data from Semantic Web Sources

-)automatic ontology generation from input data such as domain text. This thesis proposes a novel approach for learning labels of non-taxonomic;

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Adaptive Biometric Systems

of adaptive biometric systems; reviews systems for adaptive face recognition that perform self-updating of facial models using operational (unlabeled;

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Networks of Networks in Biology

theory for handling and processing multi-type large data sets. By discussing cutting-edge problems and techniques, researchers from a wide range;

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Practical Statistics for Data Scientists

belongs to Statistical machine learning methods that learn from data Unsupervised learning methods for extracting meaning from unlabeled data;

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Fraud Analytics Using Descriptive Predic

Techniques is an authoritative guidebook for setting up a comprehensive fraud detection analytics solution. Early detection is a key factor;

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Exploiting Linked Data and Knowledge Graphs in Large Organisations

This book addresses the topic of exploiting enterprise-linked data with a particular focus on knowledge construction and accessibility;

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Active Learning

the data from which it learns. An active learner may pose queries, usually in the form of unlabeled data instances to be labeled by an;

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The Unsupervised Learning Workshop

large amounts of unorganized data? The Unsupervised Learning Workshop will give you the confidence to deal with cluttered and unlabeled datasets;

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Exploiting Object Orientation to Parallelize C++ Applications

Exploiting Object Orientation to Optimize and Parallelize C]+ Applications describes some advanced C++ techniques to integrate;

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HandsOn Unsupervised Learning Using Python

artificial intelligence. Since the majority of the world's data is unlabeled, conventional supervised learning cannot be applied. Unsupervised;

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Graph-Based Semi-Supervised Learning

While labeled data is expensive to prepare, ever increasing amounts of unlabeled data is becoming widely available. In order to adapt to;

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Semi-Supervised Learning

Semi-supervised learning is an important area of machine learning. It deals with problems that involve a lot of unlabeled data and very;

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Service-Oriented Distributed Knowledge Discovery

unmanageable volumes of data by exploiting data mining and machine learning distributed models and techniques in service-oriented infrastructures;

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Service-Oriented Distributed Knowledge Discovery

unmanageable volumes of data by exploiting data mining and machine learning distributed models and techniques in service-oriented infrastructures;

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Semi-Supervised Learning and Domain Adaptation in Natural Language Processing

of these algorithms can often be improved by exploiting the marginal distribution of large amounts of unlabeled data. One reason for that is data;

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Exploiting Seismic Waveforms

Exploiting Seismic Waveforms introduces a range of recent developments in seismology including the application of correlation techniques;

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Exploiting Seismic Waveforms

Exploiting Seismic Waveforms introduces a range of recent developments in seismology including the application of correlation techniques;

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Power-Efficient High-Speed Parallel-Sampling ADCs for Broadband Multi-carrier Systems

authors focus on exploiting the a-priori knowledge of the system/application to develop enhancement techniques for ADCs, with particular emphasis;

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A New Framework for Semisupervised and Multitask Learning

Labeling image collections is a tedious and time consuming task, especially when multiple labels have to be chosen for each image. On the;

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Process Mining in Healthcare: Evaluating and Exploiting Operational Healthcare Processes

healthcare reference model that outlines all the different classes of data that are potentially available for process mining in healthcare and the;

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Predictive Modeling Applications In Actu

Predictive modeling involves the use of data to forecast future events. It relies on capturing relationships between explanatory variables;

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Analytics in a Big Data World

data, analytics and its applications in e.g. credit risk, marketing, and fraud to provide a clear roadmap for organizations that want to use;

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Workbook and Lab Manual for Sonography - Revised Reprint

Structure and Function, 3rd Edition, this workbook and lab manual includes exercises and unlabeled illustr ;

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Geospatial Techniques for Managing Environmental Resources

Geospatial Information is spatial data concerning a place or, in space, collected in real time. Geospatial techniques together with remote;

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