We present a new and efficient semi-supervised training method for parameter estimation and feature selection in conditional random fields (CRFs). In real-world applications suc...
Qualitative models are often a useful abstraction of the physical world. Learning qualitative models from numerical data sible way to obtain such an abstraction. We present a new ...
Jure Zabkar, Martin Mozina, Ivan Bratko, Janez Dem...
We propose a novel semi-supervised method for building a statistical model that represents the relationship between sounds and text labels (“tags”). The proposed method, named...
Jun Takagi, Yasunori Ohishi, Akisato Kimura, Masas...
Seed sampling is critical in semi-supervised learning. This paper proposes a clusteringbased stratified seed sampling approach to semi-supervised learning. First, various clusteri...
In this paper, we exploit the problem of inferring images’ semantic concepts from community-contributed images and their associated noisy tags. To infer the concepts more accura...