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From hierarchies to metrics: Learning nonlinear models of semantic association
(2017)
Modeling the degree of semantic similarity or dissimilarity between instances is one of the most elemental problems in machine learning and data mining. With a strong enough similarity model, other problems such as ...
Towards Formalizing Cyber-Empathic Design—A Data Driven Framework for Product Design
(2017)
A critical task in product design is mapping information from consumer to design space. Currently, this process largely depends on designers identifying and mapping psychological and consumer level factors to engineered ...
From hierarchies to metrics: Learning nonlinear models of semantic association
(2017)
Modeling the degree of semantic similarity or dissimilarity between instances is one of the most elemental problems in machine learning and data mining. With a strong enough similarity model, other problems such as ...
Methodologies for Learning Robust Feature Representations
(2017)
In order to accurately draw inferences and make predictions based on a given set of data samples, one needs to find a suitable feature representation that efficiently models the underlying data manifold. The model should ...
Metadata Analysis in Unstructured Documents Using Classical and Deep Learning Methods
(2017)
Metadata by definition is any set of data that describes and provides information about other data. Specifically, document metadata entails any information that can better represent, or guide in the improved understanding ...