This paper discusses a general framework called FLARE, that integrates inductive learning using prior knowledge together with reasoning in a non-recursive, propositional setting. FLARE learns incrementally by continually revising its knowledge base in the light of new evidence. Prior knowledge is generally given by a teacher and takes the form of pre-encoded rules. Simple defaults, combined wit...

End users can find topic model results difficult to interpret and evaluate. To address user needs, we present a semi-supervised hierarchical Dirichlet process for topic modeling that incorporates user-defined prior knowledge. Applied to a large electronic dataset, the generated topics are more fine-grained, more distinct, and align better with users’ assignments of topics to documents.

New concepts can be learned by statistical associations, as well as by relevant existing knowledge. We examined the interaction of these two processes by manipulating exemplar frequency and thematic knowledge and considering their interaction through computational modeling. Exemplar frequency affects category learning, with high-frequency items learned more quickly than low-frequency items, and...

Prior-knowledge use in neural networks, for example, knowledge of a physical system, allows network training to be tailored specific problems. Literature shows that prior-knowledge enhances predictive performance. Research date focuses on parametric optimization rather than structure optimization. We present new framework optimize the using prior-knowledge. This is achieved through optimizing n...

Estimating three-dimensional (3D) human poses from the positions of two-dimensional (2D) joints has shown promising results. However, using 2D joint coordinates as input loses more information than image-based approaches and results in ambiguity. To overcome this problem, we combine bone length camera parameters with for input. This combination is discriminative that it can improve accuracy mod...