Distributed learning for Random Vector Functional-Link networks

Scardapane, Simone; Wang, Dianhui; Panella, Massimo; Uncini, Aurelio

doi:10.1016/j.ins.2015.01.007

Cited by 146 publications

(64 citation statements)

References 27 publications

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“…mainly focus on data analysis major concern with decision mechanism The latest progress based on [14]: Based on the statistical learning tools for big data analysis proposed by Slavakis et al in [14], a lot of new study work has emerged. For example, in [113], two distributed learning algorithms for training random vector functional-link (RVFL) networks through interconnected nodes were presented, where training data were distributed under a decentralized information structure. To tackle the huge-scale convex and nonconvex big data optimization problems, a novel parallel, hybrid random/deterministic decomposition scheme with the power of dictionary learning was investigated in [114].…”

Section: The Latest Research Progressmentioning

confidence: 99%

A survey of machine learning for big data processing

Qiu

Ding

et al. 2016

EURASIP J. Adv. Signal Process.

637

361

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There is no doubt that big data are now rapidly expanding in all science and engineering domains. While the potential of these massive data is undoubtedly significant, fully making sense of them requires new ways of thinking and novel learning techniques to address the various challenges. In this paper, we present a literature survey of the latest advances in researches on machine learning for big data processing. First, we review the machine learning techniques and highlight some promising learning methods in recent studies, such as representation learning, deep learning, distributed and parallel learning, transfer learning, active learning, and kernel-based learning. Next, we focus on the analysis and discussions about the challenges and possible solutions of machine learning for big data. Following that, we investigate the close connections of machine learning with signal processing techniques for big data processing. Finally, we outline several open issues and research trends.

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Section: The Latest Research Progressmentioning

confidence: 99%

A survey of machine learning for big data processing

Qiu

Ding

et al. 2016

EURASIP J. Adv. Signal Process.

637

361

View full text Add to dashboard Cite

show abstract

“…The solution to problem (8) cannot be obtained in closed form anymore, however, many efficient methods are available to solve it [33].…”

Section: B Training the Esnmentioning

confidence: 99%

“…Among them, we can cite distributed protocols for support vector machines [7], functional-link networks [8], linear neurons [9], adaptive resonance theory (ART) networks [10], and many others. However, as we argued in [11], what is needed in many contexts is a distributed training algorithm for recurrent neural networks (RNNs).…”

Section: Introductionmentioning

confidence: 99%

Distributed Reservoir Computing with Sparse Readouts [Research Frontier]

Scardapane

Panella

Comminiello

et al. 2016

IEEE Comput. Intell. Mag.

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In a network of agents, a widespread problem is the need to estimate a common underlying function starting from locally distributed measurements. Real-world scenarios may not allow the presence of centralized fusion centers, requiring the development of distributed, message-passing implementations of the standard machine learning training algorithms. In this paper, we are concerned with the distributed training of a particular class of recurrent neural networks, namely echo state networks (ESNs). In the centralized case, ESNs have received considerable attention, due to the fact that they can be trained with standard linear regression routines. Based on this observation, in our previous work we have introduced a decentralized algorithm, framed in the distributed optimization field, in order to train an ESN. In this paper, we focus on an additional sparsity property of the output layer of ESNs, allowing for very efficient implementations of the resulting networks. In order to evaluate the proposed algorithm, we test it on two well-known prediction benchmarks, namely the Mackey-Glass chaotic time series and the 10th order nonlinear auto regressive moving average (NARMA) system.

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“…At this stage, a generic classifier conceived to work in a vector space can easily deal with the classification task. We decided to use a k-NN classifier, but different methods could be adopted, for example, several types of single layer feed-forward networks (e.g., neurofuzzy networks or random vector functional-links [34,38]). As in the case of the Gk-NN classifier, the parameter k is chosen a-priori by the user.…”

Section: Classification By Embedding In a Vector Spacementioning

confidence: 99%

Granular Computing Techniques for Classification and Semantic Characterization of Structured Data

et al. 2015

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We propose a system able to synthesize automatically a classification model and a set of interpretable decision rules defined over a set of symbols, corresponding to frequent substructures of the input dataset. Given a preprocessing procedure which maps every input element into a fully labeled graph, the system solves the classification problem in the graph domain. The extracted rules are then able to characterize semantically the classes of the problem at hand. The structured data that we consider in this paper are images coming from classification datasets: they represent an effective proving ground for studying the ability of the system to extract interpretable classification rules. For this particular input domain, the preprocessing procedure is based on a flexible segmentation algorithm whose behavior is defined by a set of parameters. The core inference engine uses a parametric graph edit dissimilarity measure. A genetic algorithm is in charge of selecting suitable values for the parameters, in order to synthesize a classification model based on interpretable rules which maximize the generalization capability of the model. Decision rules are defined over a set of information granules in the graph domain, identified by a frequent substructures miner. We compare the system with two other state-of-the-art graph classifiers, evidencing both its main strengths and limits

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Distributed learning for Random Vector Functional-Link networks

Cited by 146 publications

References 27 publications

A survey of machine learning for big data processing

A survey of machine learning for big data processing

Distributed Reservoir Computing with Sparse Readouts [Research Frontier]

Granular Computing Techniques for Classification and Semantic Characterization of Structured Data

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