In the first part, we deal with the unlabeled data that are in good quality and follow the conditions of semi-supervised learning. Firstly, we present a novel method for Transductive Support Vector Machine (TSVM) by relaxing the unknown labels to the continuous variables and reducing the non-convex optimization problem to a convex semi-definite programming problem. In contrast to the previous relaxation method which involves O (n2) free parameters in the semi-definite matrix, our method takes advantage of reducing the number of free parameters to O (n), so that we can solve the optimization problem more efficiently. In addition, the proposed approach provides a tighter convex relaxation for the optimization problem in TSVM. Empirical studies on benchmark data sets demonstrate that the proposed method is more efficient than the previous semi-definite relaxation method and achieves promising classification results comparing with the state-of-the-art methods. Our second contribution is an extended level method proposed to efficiently solve the multiple kernel learning (MKL) problems. In particular, the level method overcomes the drawbacks of both the Semi-Infinite Linear Programming (SILP) method and the Subgradient Descent (SD) method for multiple kernel learning. Our experimental results show that the level method is able to greatly reduce the computational time of MKL over both the SD method and the SILP method. Thirdly, we discuss the connection between two fundamental assumptions in semi-supervised learning. More specifically, we show that the loss on the unlabeled data used by TSVM can be essentially viewed as an additional regularizer for the decision boundary. We further show that this additional regularizer induced by the TSVM is closely related to the regularizer introduced by the manifold regularization. Both of them can be viewed as a unified regularization framework for semi-supervised learning. / In the second part, we discuss how to employ the unlabeled data for building reliable classification systems in three scenarios: (1) only poorly-related unlabeled data are available, (2) good quality unlabeled data are mixed with irrelevant data and there are no prior knowledge on their composition, and (3) no unlabeled data are available but can be achieved from the Internet for text categorization. We build several frameworks to deal with the above cases. Firstly, we present a study on how to deal with the weakly-related unlabeled data, called the Supervised Self-taught Learning framework, which can transfer knowledge from the unlabeled data actively. The proposed model is able to select those discriminative features or representations, which are more appropriate for classification. Secondly, we also propose a novel framework that can learn from a mixture of unlabeled data, where good quality unlabeled data are mixed with unlabeled irrelevant samples. Moreover, we do not need the prior knowledge on which data samples are relevant or irrelevant. Consequently it is significantly different from the recent framework of semi-supervised learning with universum and the framework of Universum Support Vector Machine. As an important contribution, we have successfully formulated this new learning approach as a Semi-definite Programming problem, which can be solved in polynomial time. A series of experiments demonstrate that this novel framework has advantages over the semi-supervised learning on both synthetic and real data in many facets. Finally, for third scenario, we present a general framework for semi-supervised text categorization that collects the unlabeled documents via Web search engines and utilizes them to improve the accuracy of supervised text categorization. Extensive experiments have demonstrated that the proposed semi-supervised text categorization framework can significantly improve the classification accuracy. Specifically, the classification error is reduced by 30% averaged on the nine data sets when using Google as the search engine. / We consider the problem of learning from both labeled and unlabeled data through the analysis on the quality of the unlabeled data. Usually, learning from both labeled and unlabeled data is regarded as semi-supervised learning, where the unlabeled data and the labeled data are assumed to be generated from the same distribution. When this assumption is not satisfied, new learning paradigms are needed in order to effectively explore the information underneath the unlabeled data. This thesis consists of two parts: the first part analyzes the fundamental assumptions of semi-supervised learning and proposes a few efficient semi-supervised learning models; the second part discusses three learning frameworks in order to deal with the case that unlabeled data do not satisfy the conditions of semi-supervised learning. / Xu, Zenglin. / Advisers: Irwin King; Michael R. Lyu. / Source: Dissertation Abstracts International, Volume: 70-09, Section: B, page: . / Thesis (Ph.D.)--Chinese University of Hong Kong, 2009. / Includes bibliographical references (leaves 158-179). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Electronic reproduction. [Ann Arbor, MI] : ProQuest Information and Learning, [200-] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstracts in English and Chinese. / School code: 1307.

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Reinforcement Learning (RL) is the study of agents that learn optimal behavior by interacting with and receiving rewards and punishments from an unknown environment. RL agents typically do this by learning value functions that assign a value to each state (situation) or to each state-action pair. Recently, there has been a growing interest in using hierarchical methods to cope with the complexity that arises due to the huge number of states found in most interesting real-world problems. Hierarchical methods seek to reduce this complexity by the use of temporal and state abstraction. Like most RL methods, most hierarchical RL methods optimize the discounted total reward that the agent receives. However, in many domains, the proper criteria to optimize is the average reward per time step. In this thesis, we adapt the concepts of hierarchical and recursive optimality, which are used to describe the kind of optimality achieved by hierarchical methods, to the average reward setting and show that they coincide under a condition called Result Distribution Invariance. We present two new model-based hierarchical RL methods, HH-learning and HAH-learning, that are intended to optimize the average reward. HH-learning is a hierarchical extension of the model-based, average-reward RL method, H-learning. Like H-learning, HH-learning requires exploration in order to learn correct domain models and optimal value function. HH-learning can be used with any exploration strategy whereas HAH-learning uses the principle of "optimism under uncertainty", which gives it a built-in "auto-exploratory" feature. We also give the hierarchical and auto-exploratory hierarchical versions of R-learning, a model-free average reward method, and a hierarchical version of ARTDP, a model-based discounted total reward method. We compare the performance of the "flat" and hierarchical methods in the task of scheduling an Automated Guided Vehicle (AGV) in a variety of settings. The results show that hierarchical methods can take advantage of temporal and state abstraction and converge in fewer steps than the flat methods. The exception is the hierarchical version of ARTDP. We give an explanation for this anomaly. Auto-exploratory hierarchical methods are faster than the hierarchical methods with ��-greedy exploration. Finally, hierarchical model-based methods are faster than hierarchical model-free methods. / Graduation date: 2003

Thesis (Ph. D.)--Rutgers University, 2009. / "Graduate Program in Computer Science." Includes bibliographical references (p. 100-104).

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Thesis (M.S.)--Oregon State University, 2004. / Typescript (photocopy). Includes bibliographical references (leaves 33-34). Also available on the World Wide Web.

Thesis (Ph. D.)--Oregon State University, 2010. / Printout. Includes bibliographical references (leaves 82-87). Also available on the World Wide Web.

Thesis (Ph. D.)--University of Texas at Austin, 2006. / Vita. Includes bibliographical references.