The multi-armed bandit problem has attracted remarkable attention in the machine learning community and many efficient algorithms have been proposed to handle the so-called exploitation-exploration dilemma in various bandit setups. At the same time, significantly less effort has been devoted to adapting bandit algorithms to particular architectures, such as sensor networks, multi-core machines, or peer-to-peer (P2P) environments, which could potentially speed up their convergence. Our goal is to adapt stochastic bandit algorithms to P2P networks. In our setup, the same set of arms is available in each peer. In every iteration each peer can pull one arm independently of the other peers, and then some limited communication is possible with a few random other peers. As our main result, we show that our adaptation achieves a linear speedup in terms of the number of peers participating in the network. More precisely, we show that the probability of playing a suboptimal arm at a peer in iteration t = Ω( \log N ) is proportional to 1/(Nt) where N denotes the number of peers. The theoretical results are supported by simulation experiments showing that our algorithm scales gracefully with the size of network.

SUMMARY Machine learning over fully distributed data poses an important problem in peer‐to‐peer applications. In this model, we have one data record at each network node but without the possibility to move raw data because of privacy considerations. For example, user profiles, ratings, history, or sensor readings can represent this case. This problem is difficult because there is no possibility to learn local models; the system model offers almost no guarantee for reliability, yet the communication cost needs to be kept low. Here, we propose gossip learning, a generic approach that is based on multiple models taking random walks over the network in parallel, while applying an online learning algorithm to improve themselves, and getting combined via ensemble learning methods. We present an instantiation of this approach for the case of classification with linear models. Our main contribution is an ensemble learning method, which—through the continuous combination of the models in the network—implements a virtual weighted voting mechanism over an exponential number of models at practically no extra cost as compared with independent random walks. We prove the convergence of the method theoretically, and perform extensive experiments on benchmark data sets. Our experimental analysis demonstrates the performance and robustness of the proposed approach. Copyright © 2012 John Wiley & Sons, Ltd.

OBJECTIVE In this study the authors describe the system submitted by the team of University of Szeged to the second i2b2 Challenge in Natural Language Processing for Clinical Data. The challenge focused on the development of automatic systems that analyzed clinical discharge summary texts and addressed the following question: "Who's obese and what co-morbidities do they (definitely/most likely) have?". Target diseases included obesity and its 15 most frequent comorbidities exhibited by patients, while the target labels corresponded to expert judgments based on textual evidence and intuition (separately). DESIGN The authors applied statistical methods to preselect the most common and confident terms and evaluated outlier documents by hand to discover infrequent spelling variants. The authors expected a system with dictionaries gathered semi-automatically to have a good performance with moderate development costs (the authors examined just a small proportion of the records manually). MEASUREMENTS Following the standard evaluation method of the second Workshop on challenges in Natural Language Processing for Clinical Data, the authors used both macro- and microaveraged Fbeta=1 measure for evaluation. RESULTS The authors submission achieved a microaverage F(beta=1) score of 97.29% for classification based on textual evidence (macroaverage F(beta=1) = 76.22%) and 96.42% for intuitive judgments (macroaverage F(beta=1) = 67.27%). CONCLUSIONS The results demonstrate the feasibility of the authors approach and show that even very simple systems with a shallow linguistic analysis can achieve remarkable accuracy scores for classifying clinical records on a limited set of concepts.

Here we propose a novel machine learning method for time series forecasting which is based on the widely-used Least Squares Support Vector Machine (LS-SVM) approach. The objective function of our method contains a weighted variance minimization part as well. This modification makes the method more efficient in time series forecasting, as this paper will show. The proposed method is a generalization of the well-known LS-SVM algorithm. It has similar advantages like the applicability of the kernel-trick, it has a linear and unique solution, and a short computational time, but can perform better in certain scenarios. The main purpose of this paper is to introduce the novel Variance Minimization Least Squares Support Vector Machine (VMLS-SVM) method and to show its superiority through experimental results using standard benchmark time series prediction datasets.