Qiang Yang

TL;DR: A novel cross-domain text classification algorithm which extends the traditional probabilistic latent semantic analysis (PLSA) algorithm to integrate labeled and unlabeled data, which come from different but related domains, into a unified probabilism model, called Topic-bridged PLSA, or TPLSA.

TL;DR: This paper develops refinement search semantics for planning, provides a generalized algorithm for refinement planning, and shows that planners that search in the space of (partial) plans are specific instantiations of this algorithm.

TL;DR: This paper gives empirical evidence that ideal Web-page summaries generated by human editors can indeed improve the performance of Web- page classification algorithms and proposes a new Web summarization-based classification algorithm that achieves an approximately 8.8% improvement over pure-text based methods.

TL;DR: A novel learning algorithm is proposed that builds location-estimation systems based on a small fraction of the calibration data that traditional techniques require and a collection of user traces that can be cheaply obtained to reduce both the sampling time and the number of locations sampled in constructing a radio map.

TL;DR: This paper proposes a discriminant tensor criterion (DTC), whereby multiple interrelated lower-dimensional discriminative subspaces are derived for feature selection and an algorithm discriminant analysis with tensor representation (DATER), which has the potential to outperform the traditional subspace learning algorithms, especially in the small sample size cases.