Constrained Conditional Model: Fundamentals and Applications
Tietoa tästä äänikirjasta
What Is Constrained Conditional Model
A constrained conditional model, also known as a constrained conditional model (CCM), is a paradigm for machine learning and inference that enhances the learning of conditional models by applying declarative constraints. It is possible to utilize the constraint as a mechanism for incorporating expressive prior knowledge into the model and for instructing the learnt model to bias the assignments it generates to satisfy the constraints. While preserving the modularity and tractability of training and inference, the framework may be utilized to enable decisions in an expressive output space.
How You Will Benefit
(I) Insights, and validations about the following topics:
Chapter 1: Constrained conditional model
Chapter 2: Machine learning
Chapter 3: Natural language processing
Chapter 4: Natural language generation
Chapter 5: Feature engineering
Chapter 6: Constrained optimization
Chapter 7: Textual entailment
Chapter 8: Transliteration
Chapter 9: Structured prediction
Chapter 10: Semantic role labeling
(II) Answering the public top questions about constrained conditional model.
(III) Real world examples for the usage of constrained conditional model in many fields.
(IV) 17 appendices to explain, briefly, 266 emerging technologies in each industry to have 360-degree full understanding of constrained conditional model' technologies.
Who This Book Is For
Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of constrained conditional model.
