Automatic Document Organization in a P2P Environment

Siersdorfer, Stefan; Sizov, Sergej

doi:10.1007/11735106_24

Cited by 22 publications

(25 citation statements)

References 21 publications

(21 reference statements)

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“…Examples include the EM algorithm [17], LDA [2] or PageRank [13]. Numerous other P2P machine learning algorithms have also been proposed, as in [18,25]. A survey of many additional ideas can be found in [7].…”

Section: Background and Related Workmentioning

confidence: 99%

Peer-to-Peer Multi-class Boosting

Istv

Hegedűs

Busa-Fekete

et al. 2012

Euro-Par 2012 Parallel Processing

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Abstract. We focus on the problem of data mining over large-scale fully distributed databases, where each node stores only one data record. We assume that a data record is never allowed to leave the node it is stored at. Possible motivations for this assumption include privacy or a lack of a centralized infrastructure. To tackle this problem, earlier we proposed the generic gossip learning framework (GoLF), but so far we have studied only basic linear algorithms. In this paper we implement the well-known boosting technique in GoLF. Boosting techniques have attracted growing attention in machine learning due to their outstanding performance in many practical applications. Here, we present an implementation of a boosting algorithm that is based on FilterBoost. Our main algorithmic contribution is a derivation of a pure online multi-class version of FilterBoost, so that it can be employed in GoLF. We also propose improvements to GoLF, with the aim of maximizing the diversity of the evolving models gossiped in the network, a feature that we show to be important. We evaluate the robustness and the convergence speed of the algorithm empirically over three benchmark databases. We compare the algorithm with the sequential AdaBoost algorithm and we test its performance in a failure scenario involving message drop and delay, and node churn.

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Section: Background and Related Workmentioning

confidence: 99%

Peer-to-Peer Multi-class Boosting

Istv

Hegedűs

Busa-Fekete

et al. 2012

Euro-Par 2012 Parallel Processing

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“…These algorithms are simple and robust, but are capable of calculating only simple functions such as the average. Nevertheless, these simple functions can serve as key components for more sophisticated methods, such as the EM algorithm [73], unsupervised learners [109], or the collaborative filtering based recommender algorithms [10,51,92,117]. Usually, these approaches use other well-studied P2P services like some kind of overlay support, for example, T-MAN [61] (for more details related to the T-MAN protocol, please see Alg.…”

Section: Related Workmentioning

confidence: 99%

“…In the past few years there has been an increasing number of proposals for P2P machine learning algorithms as well, like those in [5,6,7,34,56,77,109]. The usual assumption in these studies is that a peer has a subset of the training data on which a model can be learnt locally.…”

Section: Related Workmentioning

confidence: 99%

Applications of Support Vector-Based Learning

Ormándi¹

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PrefaceAn increasing amount of data is stored in electronic form. This phenomenon increases the need of the methods that help to access the useful pieces of information that are hidden in ocean of the collected data. That is, methods to perform automated data processing are needed. The role of machine learning is to provide methods that support this progress, i.e. it provides algorithms that can automatically discover those hidden and non-trivial patterns in data that are useful for us.In the focus of this thesis stands a special family of machine learning algorithms referred to as support vector-based learners. These algorithms are all based on the so-called maximal margin heuristic, which helps to select the final model from the suitable ones preserving the generalization ability of the model. These methods are quite general, and they have a numerous amazing properties like generalization capability or robustness to noisy data. However, to apply them to a specific task it is needed to adapt them to that particular task. This is challenging since the inappropriate adaptation can result in a drastic decrease in prediction performance or in the generalization capability; or cause a computationally infeasible situation (e.g. huge computational complexity or untreatable network load in a distributed setting).The goal of this thesis is to examine the suitability and adaptability of ii various support vector-based learning methods in numerous learning tasks and environments. During this, novel techniques and ideas, which are less frequent or maybe surprising, are investigated and discussed. The considered tasks themselves touch a wide range problems like time series forecasting, opinion mining, collaborative filtering, and the common binary classification problem. An uncommon computational environment is also concerned, namely the fully distributed environment.Róbert Ormándi, July 2013.

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“…Nevertheless, these simple functions can serve as key components for more sophisticated methods, such as the EM 3.2. GOSSIP LEARNING algorithm [69], unsupervised learners [101] and the collaborative filtering-based recommender algorithms [14,46,87,108]. However, here we seek to provide a rather generic approach that covers a wide range of machine learning models, while maintaining robustness and simplicity.…”

Section: Related Workmentioning

confidence: 99%

“…In the past few years there have been an increasing number of proposals for P2P machine learning algorithms as well, like those in [5,6,7,28,54,77,101].…”

Section: Related Workmentioning

confidence: 99%

Gossip-Based Machine Learning in Fully Distributed Environments

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Automatic Document Organization in a P2P Environment

Cited by 22 publications

References 21 publications

Peer-to-Peer Multi-class Boosting

Peer-to-Peer Multi-class Boosting

Applications of Support Vector-Based Learning

Gossip-Based Machine Learning in Fully Distributed Environments

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