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In this paper, we propose a novel machine learning framework called “Online Transfer Learning” (OTL), which aims to attack an online learning task on a target domain by transferring knowledge from some source domain. We do not assume data in the target domain follows the same distribution as that in the source domain, and the motivation of our work is to enhance a supervised online learning task on a target domain by exploiting the existing knowledge that had been learnt from training data in source domains. OTL is in general very challenging since data in both source and target domains not only can be different in their class distributions, but also can be diverse in their feature representations. As a first attempt to this new research problem, we investigate two different settings of OTL: (i) OTL on homogeneous domains of common feature space, and (ii) OTL across heterogeneous domains of different feature spaces. For each setting, we propose effective OTL algorithms to solve online classification tasks, and show some theoretical bounds of the algorithms. In addition, we also apply the OTL technique to attack the challenging online learning tasks with concept-drifting data streams. Finally, we conduct extensive empirical studies on a comprehensive testbed, in which encouraging results validate the efficacy of our techniques. [ABSTRACT FROM AUTHOR]

Projected gradient descent has been proved efficient in many optimization and machine learning problems. The weighted ℓ 1 ball has been shown effective in sparse system identification and features selection. In this paper we propose three new efficient algorithms for projecting any vector of finite length onto the weighted ℓ 1 ball. The first two algorithms have a linear worst case complexity. The third one has a highly competitive performances in practice but the worst case has a quadratic complexity. These new algorithms are efficient tools for machine learning methods based on projected gradient descent such as compressed sensing, feature selection. We illustrate this effectiveness by adapting an efficient compressed sensing algorithm to weighted projections. We demonstrate the efficiency of our new algorithms on benchmarks using very large vectors. For instance, it requires only 8 ms, on an Intel I7 3rd generation, for projecting vectors of size 107. [ABSTRACT FROM AUTHOR]

Abstract: Applying learning techniques to acquire action models is an area of intense research interest. Most previous work in this area has assumed that there is a significant amount of training data available in a planning domain of interest. However, it is often difficult to acquire sufficient training data to ensure the learnt action models are of high quality. In this paper, we seek to explore a novel algorithm framework, called TRAMP, to learn action models with limited training data in a target domain, via transferring as much of the available information from other domains (called source domains) as possible to help the learning task, assuming action models in source domains can be transferred to the target domain. TRAMP transfers knowledge from source domains by first building structure mappings between source and target domains, and then exploiting extra knowledge from Web search to bridge and transfer knowledge from sources. Specifically, TRAMP first encodes training data with a set of propositions, and formulates the transferred knowledge as a set of weighted formulas. After that it learns action models for the target domain to best explain the set of propositions and the transferred knowledge. We empirically evaluate TRAMP in different settings to see their advantages and disadvantages in six planning domains, including four International Planning Competition (IPC) domains and two synthetic domains. [Copyright &y& Elsevier]

Abstract: In this paper, we propose the MIML (Multi-Instance Multi-Label learning) framework where an example is described by multiple instances and associated with multiple class labels. Compared to traditional learning frameworks, the MIML framework is more convenient and natural for representing complicated objects which have multiple semantic meanings. To learn from MIML examples, we propose the MimlBoost and MimlSvm algorithms based on a simple degeneration strategy, and experiments show that solving problems involving complicated objects with multiple semantic meanings in the MIML framework can lead to good performance. Considering that the degeneration process may lose information, we propose the D-MimlSvm algorithm which tackles MIML problems directly in a regularization framework. Moreover, we show that even when we do not have access to the real objects and thus cannot capture more information from real objects by using the MIML representation, MIML is still useful. We propose the InsDif and SubCod algorithms. InsDif works by transforming single-instances into the MIML representation for learning, while SubCod works by transforming single-label examples into the MIML representation for learning. Experiments show that in some tasks they are able to achieve better performance than learning the single-instances or single-label examples directly. [Copyright &y& Elsevier]