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.
General
Is the information for this product incomplete, wrong or inappropriate?
Let us know about it.
Does this product have an incorrect or missing image?
Send us a new image.
Is this product missing categories?
Add more categories.
Review This Product
No reviews yet - be the first to create one!
Welcome to Loot.co.za!
Sign in / Register |Wishlists & Gift Vouchers |Help | Advanced search
