Distributed semi-supervised support vector machines

Scardapane, Simone; Fierimonte, Roberto; Lorenzo, Paolo Di; Panella, Massimo; Uncini, Aurelio

doi:10.1016/j.neunet.2016.04.007

Cited by 54 publications

(22 citation statements)

References 33 publications

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“…Another possibility for nonlinear learning over networks is given by considering spline adaptive filters (SAFs). 8 The idea of using SAFs in a distributed environment was recently introduced in [50]. In the following we briefly describe the distributed algorithm.…”

Section: Diffusion Spline Filtersmentioning

confidence: 99%

Adaptation and Learning Over Networks for Nonlinear System Modeling

Scardapane

Chen

Richard

2018

Adaptive Learning Methods for Nonlinear System Modeling

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In this chapter, we analyze nonlinear filtering problems in distributed environments, e.g., sensor networks or peer-to-peer protocols. In these scenarios, the agents in the environment receive measurements in a streaming fashion, and they are required to estimate a common (nonlinear) model by alternating local computations and communications with their neighbors. We focus on the important distinction between single-task problems, where the underlying model is common to all agents, and multitask problems, where each agent might converge to a different model due to, e.g., spatial dependencies or other factors. Currently, most of the literature on distributed learning in the nonlinear case has focused on the single-task case, which may be a strong limitation in real-world scenarios. After introducing the problem and reviewing the existing approaches, we describe a simple kernel-based algorithm tailored for the multitask case. We evaluate the proposal on a simulated benchmark task, and we conclude by detailing currently open problems and lines of research.

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Section: Diffusion Spline Filtersmentioning

confidence: 99%

Adaptation and Learning Over Networks for Nonlinear System Modeling

Scardapane

Chen

Richard

2018

Adaptive Learning Methods for Nonlinear System Modeling

Self Cite

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“…Different from [6,24], we choose c = 1 for the second term in (1) for accuracy. Here, the training data set is not available on the central unit, but each agent i in the M -agent network has access to a stream of n i labeled samples with…”

Section: Preliminaries and Svm Formulationmentioning

confidence: 99%

Distributed classification learning based on nonlinear vector support machines for switching networks

Wang¹,

Lin²,

Qin³

2017

Kybernetika

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“…The LASSO problems are solved using a freely available implementation of the iterated ridge regression algorithm by Schmidt [50], [51]. 2 The algorithm works by approximating the L 1 term in (8), and iteratively solving the resulting ridge regression problem. Results are presented in Fig.…”

Section: B Comparisons In the Centralized Casementioning

confidence: 99%

“…Among many open research problems today, one of the most crucial is how to efficiently perform supervised inference when the data of the problem is not available on a centralized location, being distributed among a network of interconnected agents [1], [2]. Examples include peer-to-peer (P2P) networks [3], sensors [4], robotic swarms, and many others.…”

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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Distributed semi-supervised support vector machines

Cited by 54 publications

References 33 publications

Adaptation and Learning Over Networks for Nonlinear System Modeling

Adaptation and Learning Over Networks for Nonlinear System Modeling

Distributed classification learning based on nonlinear vector support machines for switching networks

Distributed Reservoir Computing with Sparse Readouts [Research Frontier]

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