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Showing 1 - 4 of
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The Painted Lady
Charles Sutton
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R563
Discovery Miles 5 630
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Ships in 10 - 15 working days
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Stingray - Prophecy (Paperback)
Gary Zeiger; Cover design or artwork by Charles Sutton
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R422
R360
Discovery Miles 3 600
Save R62 (15%)
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Ships in 10 - 15 working days
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In modern applications of machine learning, predicting a single
class label is often not enough. Instead we want to predict a large
number of variables that depend on each other, such as a class
label for every word in a document or for every region in an image.
This structured prediction problem is significantly harder than the
simple classification problem because we want to learn how the
different labels depend on each other. Conditional random fields
provide a powerful solution to this problem. They combine the
advantages of classification and graphical modeling as they join
the ability of graphical models to compactly model multivariate
data with the ability of classification methods to perform
prediction using large sets of input features. In the past ten
years, there has been an explosion of interest in CRFs with
applications as diverse as natural language processing, computer
vision, and bioinformatics. An Introduction to Conditional Random
Fields provides a comprehensive tutorial aimed at
application-oriented practitioners seeking to apply CRFs. This
survey does not assume previous knowledge of graphical modeling,
and so is intended to be useful to practitioners in a wide variety
of fields. It includes discussion of feature construction,
inference, and parameter estimation in CRFs. Additionally, the
monograph also includes sections on practical "tips of the trade"
for CRFs that are difficult to find in the published literature.
Advanced statistical modeling and knowledge representation
techniques for a newly emerging area of machine learning and
probabilistic reasoning; includes introductory material, tutorials
for different proposed approaches, and applications. Handling
inherent uncertainty and exploiting compositional structure are
fundamental to understanding and designing large-scale systems.
Statistical relational learning builds on ideas from probability
theory and statistics to address uncertainty while incorporating
tools from logic, databases and programming languages to represent
structure. In Introduction to Statistical Relational Learning,
leading researchers in this emerging area of machine learning
describe current formalisms, models, and algorithms that enable
effective and robust reasoning about richly structured systems and
data. The early chapters provide tutorials for material used in
later chapters, offering introductions to representation, inference
and learning in graphical models, and logic. The book then
describes object-oriented approaches, including probabilistic
relational models, relational Markov networks, and probabilistic
entity-relationship models as well as logic-based formalisms
including Bayesian logic programs, Markov logic, and stochastic
logic programs. Later chapters discuss such topics as probabilistic
models with unknown objects, relational dependency networks,
reinforcement learning in relational domains, and information
extraction. By presenting a variety of approaches, the book
highlights commonalities and clarifies important differences among
proposed approaches and, along the way, identifies important
representational and algorithmic issues. Numerous applications are
provided throughout.
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