Learning to Rank Among Controlled Attributes


Learning to Rank Among Controlled Attributes – To summarize our work with reference to two related problems: (1) how to model the problem of identifying a controlled attribute in high-dimensional data, (2) how to build general artificial learning models for high-dimensional data. We propose a general approach called supervised learning that is able to model the problem of identifying an ordered set of attributes, which results in a very natural and accurate estimation of the control attribute. Using a supervised model, we are able to select the best pair of attributes by applying a simple and accurate estimation algorithm to the problem of the ordering of the attributes. The estimation algorithm is based on a deep neural network. A supervised model is trained on the attribute set, and the learning algorithm learns the classifier for each attribute. The resulting model generalizes to a wide class of supervised learning tasks, such as prediction of the user’s actions, prediction of the user’s choice, and classification of the user’s choice.

We propose an efficient method for automatic semantic labeling for multi-label classification. Compared to previous techniques based on model-based approaches, the proposed method aims at representing both the semantic information that is provided by models and the discriminative semantic information that is provided by data. Based on the idea of combining Semantic Hierarchy (a.k.a Semantic Hierarchical Modeling) with a relational model, we implement a sequential-memory, multi-label classification model trained using semantic labels. The proposed model can not be used with any other semantic model, such as a supervised classifier. We further propose a new Semantic Hierarchical Model-based model for semi-supervised classification, incorporating two different models based on semantic labels and a classifier trained on Semantic Hierarchy, and a hierarchical model trained on the Semantic Hierarchical Model.

Hessian Distance Regularization via Nonconvex Sparse Estimation

Robust Sparse Clustering

Learning to Rank Among Controlled Attributes

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  • A Deep Learning Model for Multiple Tasks Teleoperation

    Probabilistic Models for Constraint-Free Semantic Parsing with Sparse Linear ProgrammingWe propose an efficient method for automatic semantic labeling for multi-label classification. Compared to previous techniques based on model-based approaches, the proposed method aims at representing both the semantic information that is provided by models and the discriminative semantic information that is provided by data. Based on the idea of combining Semantic Hierarchy (a.k.a Semantic Hierarchical Modeling) with a relational model, we implement a sequential-memory, multi-label classification model trained using semantic labels. The proposed model can not be used with any other semantic model, such as a supervised classifier. We further propose a new Semantic Hierarchical Model-based model for semi-supervised classification, incorporating two different models based on semantic labels and a classifier trained on Semantic Hierarchy, and a hierarchical model trained on the Semantic Hierarchical Model.


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