Add to ORKGStructured prediction (SP) problems are characterized by strong interdependence among the output variables, usually with sequential, graphical, or combinatorial structure [17, 7]. Obtaining good predictors often requires a large effort in feature/kernel design and tuning (usually done via cross-validation). Because discriminative training of structured predictors can be quite slow, specially i
International audienceSupervised learning is about learning functions given a set of input and corre...
Conditional random fields (CRF) and structural support vector machines (structural SVM) are two stat...
The major challenge in designing a discriminative learning algorithm for predicting structured data ...
Structured prediction (SP) problems are characterized by strong interdependence among the output var...
Training structured predictors often requires a considerable time selecting features or tweaking the...
The goal of structured prediction is to build machine learning models that predict relational inform...
Complex tasks such as sequence labeling, collective classification, and activity recognition involve...
We consider the task of structured data prediction. Over the last few years, there has been an abund...
Discriminative techniques, such as conditional random fields (CRFs) or structure aware maximum-margi...
We present a series of learning algorithms and theoretical guarantees for designing accurate en-semb...
We present a series of learning algorithms and theoretical guarantees for designing accurate en-semb...
Structured prediction problems are one of the fundamental tools in machine learning. In order to fac...
A powerful and flexible approach to structured prediction consists in embedding the structured objec...
Structured output prediction in machine learning is the study of learning to predict complex objects...
Machine learning practitioners often face a fundamental trade-off between expressiveness and computa...
International audienceSupervised learning is about learning functions given a set of input and corre...
Conditional random fields (CRF) and structural support vector machines (structural SVM) are two stat...
The major challenge in designing a discriminative learning algorithm for predicting structured data ...
Structured prediction (SP) problems are characterized by strong interdependence among the output var...
Training structured predictors often requires a considerable time selecting features or tweaking the...
The goal of structured prediction is to build machine learning models that predict relational inform...
Complex tasks such as sequence labeling, collective classification, and activity recognition involve...
We consider the task of structured data prediction. Over the last few years, there has been an abund...
Discriminative techniques, such as conditional random fields (CRFs) or structure aware maximum-margi...
We present a series of learning algorithms and theoretical guarantees for designing accurate en-semb...
We present a series of learning algorithms and theoretical guarantees for designing accurate en-semb...
Structured prediction problems are one of the fundamental tools in machine learning. In order to fac...
A powerful and flexible approach to structured prediction consists in embedding the structured objec...
Structured output prediction in machine learning is the study of learning to predict complex objects...
Machine learning practitioners often face a fundamental trade-off between expressiveness and computa...
International audienceSupervised learning is about learning functions given a set of input and corre...
Conditional random fields (CRF) and structural support vector machines (structural SVM) are two stat...
The major challenge in designing a discriminative learning algorithm for predicting structured data ...