With the growth of the digital advertising market, it has become more important than ever to target the desired audiences. Among various demographic traits, gender information plays a key role in precisely targeting the potential consumers in online advertising and ecommerce. However, such personal information is generally unavailable to digital media sellers. In this paper, we propose a novel task-specific multi-task learning algorithm to predict users' gender information from their video viewing behaviors. To detect as many desired users as possible, while controlling the Type I error rate at a user-specified level, we further propose Bayes testing and decision procedures to efficiently identify male and female users, respectively. Comprehensive experiments have justified the effectiveness and reliability of our framework.

The proliferation of objectionable information on the Internet has reached a level of serious concern. To empower end-users with the choice of blocking undesirable and offensive websites, we propose a multimodal information filter, named MORF. In this paper, we present MORF's core components: its confidence-based classifier, a Cross-bagging ensemble scheme, and multimodal classification algorithm. Empirical studies and initial statistics collected from the MORF filters deployed at sites in the U.S. and Asia show that MORF is both efficient and effective, due to our classification methods.

In this paper we discuss how to use a one-dimensional classifier for solving high dimensional classification problems. We propose Spin Discriminant Analysis (SDA), which enables us to construct a family of new classifiers. We prove that SDA is equivalent to ridged Linear Discriminant Analysis (LDA) when two classes are Gaussians with common covariance matrices. Moreover, we prove that classification based on Parzen's window, is a special case of SDA. In addition to theoretical investigations, we conduct extensive empirical studies, implementing SDA using Support Vector Machines (SVMs) as its one-dimensional classifiers. This SVM-based SDA implementation is named SpinSVM. Our experiments show that SpinSVM outperforms traditional high dimensional classifiers like SVMs, Classification Using Spline (CUS), classification-based Parzen's window, and LDA on most standard and synthetic datasets we tested.

Various systems have been developed for supporting content-based image retrieval. Most systems make very strong assumptions in modeling users’ query concepts. However, since the information need of users can be very diverse, these assumptions may not always hold and hence can lead to poor search results. For instance, if a system assumes that the query-concept is convex but a user issues a disjunctive query, and vice versa, the search result cannot be satisfactory. In this study, we propose a method that can approximate more complex (non-convex and disjunctive) query concepts. Our method uses intelligent modeling and learning to increase query speed and accuracy. Empirical results show that our method converges consistently faster than some traditional approaches on different datasets.