Transfer learning aims to learn classifiers for a target domain by transferring knowledge from a source domain. However, due to two main issues: feature discrepancy and distribution divergence, transfer learning can be a very difficult problem in practice. In this paper, we present a framework called TLF that builds a classifier for the target domain having only few labeled training records by transferring knowledge from the source domain having many labeled records. While existing methods often focus on one issue and leave the other one for the further work, TLF is capable of handling both issues simultaneously. In TLF, we alleviate feature discrepancy by identifying shared label distributions that act as the pivots to bridge the domains. We handle distribution divergence by simultaneously optimizing the structural risk functional, joint distributions between domains, and the manifold consistency underlying marginal distributions. Moreover, for the manifold consistency we exploit its intrinsic properties by identifying k nearest neighbors of a record, where the value of k is determined automatically in TLF. Furthermore, since negative transfer is not desired, we consider only the source records that are belonging to the source pivots during the knowledge transfer. We evaluate TLF on seven publicly available natural datasets and compare the performance of TLF against the performance of eleven state-of-the-art techniques. We also evaluate the effectiveness of TLF in some challenging situations. Our experimental results, including statistical sign test and Nemenyi test analyses, indicate a clear superiority of the proposed framework over the state-of-the-art techniques.

Scaling multiagent reinforcement learning to domains with many agents is a complex problem. In particular, multiagent credit assignment becomes a key issue as the system size increases. Some multiagent systems suffer from a global reward signal that is very noisy or difficult to analyze. This makes deriving a learnable local reward signal very difficult. Difference rewards (a particular instance of reward shaping) have been used to alleviate this concern, but they remain difficult to compute in many domains. In this paper we present an approach to modeling the global reward using function approximation that allows the quick computation of local rewards. We demonstrate how this model can result in significant improvements in behavior for three congestion problems: a multiagent ``bar problem'', a complex simulation of the United States airspace, and a generic air traffic domain. We show how the model of the global reward may be either learned on- or off-line using either linear functions or neural networks. For the bar problem, we show an increase in reward of nearly 200% over learning using the global reward directly. For the air traffic problem, we show a decrease in costs of 25% over learning using the global reward directly.