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Constrained conditional model

Known as: ILP4NLP, Integer Linear Programming applications for Natural Language Processing, CCM 
A constrained conditional model (CCM) is a machine learning and inference framework that augments the learning of conditional (probabilistic or… 
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Related topics

Related topics

6 relations
Feature engineeringMarkov logic networkNatural language processingSemantic role labeling

Broader (2)

Machine learningStructured prediction

Papers overview

Semantic Scholar uses AI to extract papers important to this topic.
2017
2017
Multivariate Adaptive Sampling of Parameterized Antenna Responses
We present a robust method to adaptively construct parameterized models of the full radiation patterns of antennas and the… 
2016
2016
Constrained adaptive bias correction for satellite radiances assimilation in the ECMWF 4D-Var
This documentation was developed within the context of the EUMETSAT Satellite Application Facility on Numerical Weather… 
Highly Cited
2016
Highly Cited
2016
Constrained Iterative Feedback Tuning for Robust Control of a Wafer Stage System
Iterative feedback tuning (IFT) enables the data-driven tuning of controller parameters without the explicit need for a… 
2016
2016
Intent-Aware Diversification Using a Constrained PLSA
The intent-aware diversification framework was introduced initially in information retrieval and adopted to the context of… 
Highly Cited
2012
Highly Cited
2012
Structured learning with constrained conditional models
Making complex decisions in real world problems often involves assigning values to sets of interdependent variables where an… 
Review
2011
Review
2011
The pace of technological innovation in architecture, engineering, and construction education: integrating recent trends into the curricula
The U.S. AEC industry is faced with the ever-increasing challenge of managing the public and private facilities and… 
2011
2011
Noisy Constrained Maximum-Likelihood Linear Regression for Noise-Robust Speech Recognition
Adaptive training is a widely used technique for building speech recognition systems on nonhomogeneous training data. Recently… 
2011
2011
Function Approximation for Continuous Constrained MDPs
In this work we apply function approximation techniques to solve continuous, constrained Markov Decision Processes (MDPs). Many… 
Highly Cited
2008
Highly Cited
2008
Learning and Inference with Constraints
Probabilistic modeling has been a dominant approach in Machine Learning research. As the field evolves, thc problems of interest… 
Highly Cited
1994
Highly Cited
1994
The Role of Constraints in Hebbian Learning
Models of unsupervised, correlation-based (Hebbian) synaptic plasticity are typically unstable: either all synapses grow until… 
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