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A new learning paradigm for random vector functional-link network: RVFL

NEURAL networks 2020年 122卷 94-105页

作者： Zhang, Peng-Bo Yang, Zhi-Xin Univ Macau Fac Sci & Technol State Key Lab Internet Things Smart City Macau 999078 Peoples R China Univ Macau Fac Sci & Technol Dept Electromech Engn Macau 999078 Peoples R China

In school, a teacher plays an important role in various classroom teaching patterns. Likewise to this human learning activity, the learning using privileged information (LUPI) paradigm provides additional information generated by the teacher to 'teach' learning models during the training stage. Therefore, this novel learning paradigm is a typical Teacher-Student Interaction mechanism. This paper is the first to present a random vector functional link (RVFL) network based on the LUPI paradigm, called RVFL+. The novel RVFL+ incorporates the LUPI paradigm that can leverage additional source of information into the RVFL, which offers an alternative way to train the RVFL. Rather than simply combining two existing approaches, the newly-derived RVFL+ fills the gap between classical randomized neural networks and the newfashioned LUPI paradigm. Moreover, the proposed RVFL+ can perform in conjunction with the kernel trick for highly complicated nonlinear feature learning, termed KRVFL+. Furthermore, the statistical property of the proposed RVFL+ is investigated, and the authors present a sharp and high-quality generalization error bound based on the Rademacher complexity. Competitive experimental results on 14 real-world datasets illustrate the great effectiveness and efficiency of the novel RVFL+ and KRVFL+, which can achieve better generalization performance than state-of-the-art methods. (c) 2019 Elsevier Ltd. All rights reserved.

关键词： RVFL KRVFL Learning using privileged information The Rademacher complexity SVM random vector functional link networks

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Broad learning: A GPU-free image-based malware classification

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APPLIED SOFT COMPUTING 2024年 154卷

作者： Vasan, Danish Hammoudeh, Mohammad Alazab, Mamoun King Fahd Univ Petr & Minerals Interdisciplinary Res Ctr Intelligent Secure Syst Dhahran Saudi Arabia King Fahd Univ Petr & Minerals Informat & Comp Sci Dept Dhahran Saudi Arabia Charles Darwin Univ Fac Sci & Technol Casuarina NT Australia

In today's cybersecurity landscape, software security companies encounter a significant challenge in detecting new and unknown malware. Despite the introduction of various machine learning and deep learning tools designed to identify malicious software based on static and dynamic features, achieving the desired level of accuracy remains elusive. This challenge is exacerbated by factors such as encryption, packing, limited distribution, and uneven allocation of malware samples across different families. Moreover, deep learning techniques demand substantial time, computational resources (specifically GPUs), and expertise from data scientists for practical malware analysis. In response to these challenges, we propose a novel GPU-free approach called Imagebased Malware Classification using Broad Learning (IMCBL) to address these issues. Our method integrates visualization, feature decomposition, and broad learning architecture to enhance malware detection and classification. We convert raw malware binaries into images, reducing the necessity for extensive feature engineering. These images transform using truncated Singular Value Decomposition (SVD) to reduce the feature vector size, expediting the training process while mitigating model overfitting. The transformed feature vector is then input into our proposed Broad Learning (BL) system, which facilitates malware detection and classification. The BL architecture, structured as a flat network mapping original inputs to feature nodes and expanding the structure in enhancement nodes, ensures efficient and effective classification without the need for retraining. This dynamic and incremental learning capability sets IMCBL apart, making it superior to existing deep learning architectures. To validate our approach, we conducted extensive experiments using five benchmark malware datasets, including the Microsoft Windows malware challenge dataset, the Malimg Windows malware dataset, the IoT-Android mobile malware dataset, the

关键词： Cybersecurity Image-based Malware Classification Singular Value Decomposition Broad Learning System Deep Learning System Single layer feedforward neural networks random vector functional link networks

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Bayesian learning of feature spaces for multitask regression

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NEURAL networks 2024年 179卷 106619页

作者： Sevilla-Salcedo, Carlos Gallardo-Antolin, Ascension Gomez-Verdejo, Vanessa Parrado-Hernandez, Emilio Univ Carlos III Madrid Dept Signal Theory & Commun Leganes 28911 Madrid Spain Aalto Univ Dept Comp Sci Helsinki 02150 Finland

This paper introduces a novel approach to learn multi-task regression models with constrained architecture complexity. The proposed model, named RFF-BLR, consists of a randomised feedforward neural network with two fundamental characteristics: a single hidden layer whose units implement the random Fourier features that approximate an RBF kernel, and a Bayesian formulation that optimises the weights connecting the hidden and output layers. The RFF-based hidden layer inherits the robustness of kernel methods. The Bayesian formulation enables promoting multioutput sparsity: all tasks interplay during the optimisation to select a compact subset of the hidden layer units that serve as common non-linear mapping for every tasks. The experimental results show that the RFF-BLR framework can lead to significant performance improvements compared to the state-of-the-art methods in multitask nonlinear regression, especially in small-sized training dataset scenarios.

关键词： Kernel methods random fourier features Bayesian regression Multitask regression Extreme learning machine random vector functional link networks

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Generalized Learning vector Quantization for Classification in randomized Neural networks and Hyperdimensional Computing

Generalized Learning Vector Quantization for Classification ...

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International Joint Conference on Neural networks (IJCNN)

作者： Diao, Cameron Kleyko, Denis Rabaey, Jan M. Olshausen, Bruno A. Rice Univ Dept Comp Sci Houston TX 77005 USA Univ Calif Berkeley Berkeley CA USA Res Inst Sweden Kista Sweden Univ Calif Berkeley Berkeley Wireless Res Ctr Berkeley CA USA Univ Calif Berkeley Redwood Ctr Theoret Neurosci Berkeley CA USA

ISBN: (纸本)9780738133669

Machine learning algorithms deployed on edge devices must meet certain resource constraints and efficiency requirements. random vector functional link (RVFL) networks are favored for such applications due to their simple design and training efficiency. We propose a modified RVFL network that avoids computationally expensive matrix operations during training, thus expanding the network's range of potential applications. Our modification replaces the least-squares classifier with the Generalized Learning vector Quantization (GLVQ) classifier, which only employs simple vector and distance calculations. The GLVQ classifier can also be considered an improvement upon certain classification algorithms popularly used in the area of Hyperdimensional Computing. The proposed approach achieved state-of-the-art accuracy on a collection of datasets from the UCI Machine Learning Repository-higher than previously proposed RVFL networks. We further demonstrate that our approach still achieves high accuracy while severely limited in training iterations (using on average only 21% of the least-squares classifier computational costs).

关键词： learning vector quantization randomly connected neural networks hyperdimensional computing random vector functional link networks

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Hyperdimensional Computing for Efficient Distributed Classification with randomized Neural networks

Hyperdimensional Computing for Efficient Distributed Classif...

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International Joint Conference on Neural networks (IJCNN)

作者： Rosato, Antonello Panella, Massimo Kleyko, Denis Univ Roma La Sapienza Diet Dept Rome Italy Univ Calif Berkeley Berkeley CA USA Res Inst Sweden Kista Sweden

ISBN: (纸本)9780738133669

In the supervised learning domain, considering the recent prevalence of algorithms with high computational cost, the attention is steering towards simpler, lighter, and less computationally extensive training and inference approaches. In particular, randomized algorithms are currently having a resurgence, given their generalized elementary approach. By using randomized neural networks, we study distributed classification, which can be employed in situations were data cannot be stored at a central location nor shared. We propose a more efficient solution for distributed classification by making use of a lossy compression approach applied when sharing the local classifiers with other agents. This approach originates from the framework of hyperdimensional computing, and is adapted herein. The results of experiments on a collection of datasets demonstrate that the proposed approach has usually higher accuracy than local classifiers and getting close to the benchmark - the centralized classifier. This work can be considered as the first step towards analyzing the variegated horizon of distributed randomized neural networks.

关键词： randomized neural networks hyperdimensional computing random vector functional link networks

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Gauss–Seidel Extreme Learning Machines

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SN Computer Science 2020年第4期1卷 1-28页

作者： de Freitas, Rafaela C. Ferreira, Janderson de Lima, Sidney M. L. Fernandes, Bruno José T. Bezerra, Byron L. D. dos Santos, Wellington P. Polytechnique School of the University of Pernambuco Recife Brazil Federal University of Pernambuco Recife Brazil

Extreme learning machines (ELM) were created to simplify the training phase of single-layer feedforward neural networks, where the input weights are randomly set and the only parameter is the number of neurons in the hidden layer. These networks are also known for one-shot training using Moore–Penrose pseudo-inverse. In this work, we propose Gauss–Seidel extreme learning machine (GS-ELM), an ELM based on Gauss–Seidel iterative method to solve linear equation systems. We performed tests considering databases with different characteristics and analysed its discrimination capabilities and memory consumption in comparison to the canonical ELM and the online sequential ELM. GS-ELM presented similar discrimination capabilities, but consuming significantly less memory, turning possible its application in low-memory systems and embedded solutions. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Extreme learning machines Fast training neural networks Numerical methods random vector functional link networks Unorganized neural networks

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Impact of Probability Distribution Selection on RVFL Performance 1

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2nd International Conference on Smart Computing and Communication (SmartCom)

作者： Cao, Weipeng Gao, Jinzhu Ming, Zhong Cai, Shubin Zheng, Hua Shenzhen Univ Shenzhen 518060 Peoples R China Univ Pacific Stockton CA 95211 USA

ISBN: (数字)9783319738307

ISBN: (纸本)9783319738307;9783319738291

The initialization of input weights and hidden biases plays an important role in random vector functional link networks (RVFL). Although some optimization algorithms for initialization have been proposed in recent years, the initialization strategies of these algorithms are under the premise of the uniform distribution. In this paper, ten benchmark datasets are used to study the impact of different probability distributions (e.g., Uniform, Gaussian, and Gamma distributions) initialization on the performance of RVFL. The experimental results present some interesting observations and valuable instructions: (1) No matter whether we use Uniform, Gaussian, or Gamma distributions, RVFL initialized by the distribution with smaller variances always get lower training and testing RMSE;(2) Compared with the Uniform distribution, the Gaussian and Gamma distributions with smaller variances usually give the RVFL model better performance;(3) Regardless of the distribution, RVFL with the direct link from the input layer to the output layer has better performance than those without the link;(4) RVFL initialized by the distribution with larger variances generally needs more hidden nodes to achieve equivalent accuracy with ones having the smaller variances;(5) With the increase of distribution variances, the performance of RVFL decreases first and then remains stable.

关键词： random vector functional link networks Neural networks with random weights random initialization Probability distribution Parameter selection

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Broad Learning System: structural extensions on single-layer and multi-layer neural networks

Broad Learning System: structural extensions on single-layer...

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International Conference on Security, Pattern Analysis, and Cybernetics (ICSPAC)

作者： Liu, Zhulin Chen, C. L. Philip Univ Macau Fac Sci & Technol Dept Comp & Informat Sci Macau Peoples R China Dalian Maritime Univ Dalian Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing Peoples R China

ISBN: (纸本)9781538630167

Broad Learning System proposed recently Ill demonstrates efficient and effective learning capability. Moreover, fast incremental learning algorithms are developed in broad expansions without an entire retraining of the whole model. Compared with the systems in deep structure, the inspired system provides competitive results in classification. In this paper, the broad learning algorithms and incremental learning algorithms are applied to commonly used neural networks, such as radial basis function neural networks (RBF) and hierarchical extremal learning machine (H-ELM). For RBF, the resulting models, called BLS-RBF, are established by regarding the radial basis function as the mapping in the enhancement nodes, and additional enhancement nodes are added if the network needs expansion widely. For H-ELM, the established model, is developed for the incremental extension of multilayer structure. The developed BLS models and algorithms are very effective and efficient in classification. Finally, experimental results are presented.

关键词： Single layer feedforward neural networks random vector functional link networks broad learning system incremental learning extremal learning machine radial basis function networks hierarchical extremal learning machine

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Broad Learning System: Feature extraction based on K-means clustering algorithm

Broad Learning System: Feature extraction based on K-means c...

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4th International Conference on Information, Cybernetics and Computational Social Systems (ICCSS)

作者： Liu, Zhulin Zhou, Jin Chen, C. L. Philip Univ Macau Dept Comp & Informat Sci Fac Sci & Technol Macau Peoples R China Dalian Maritime Univ Dalian Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing Peoples R China Univ Jinan Shandong Prov Key Lab Network Based Intelligent C Jinan Shandong Peoples R China

ISBN: (纸本)9781538632574

Broad Learning System Hi proposed recently demonstrates efficient and effective learning capability. This model is also proved to be suitable for incremental learning algorithms by taking the advantages of random vector flat neural networks. In this paper, a modified BLS structure based on the K-means feature extraction is developed. Compared with the original broad learning system, acceptable performance on more complicated data set, such as CIFAR-10, is achieved. Furthermore, it is proved that the proposed model in Hi is flexible and potential in various applications.

关键词： Single layer feedforward neural networks SVD random vector functional link networks broad learning system incremental learning deep learning K-means feature representation

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Broad Learning System: a new learning paradigm and system without going deep 32

Broad Learning System: a new learning paradigm and system wi...

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32nd Youth Academic Annual Conference of Chinese Association of Automation (YAC)

作者： Chen, C. L. Philip Liu, Zhulin Univ Macau Fac Sci & Technol Macau 99999 Peoples R China Dalian Maritime Univ Dalian Liaoning Peoples R China

ISBN: (纸本)9781538629017

This paper introduces a Broad Learning System that gives a new paradigm and learning system without the need of deep architecture. In deep structure and learning, the abundant connecting parameters in filters and layers lead to a time-consuming training process. Broad Learning system, which is established as a flat network, maps the original inputs as mapped features in feature nodes and the structure is expanded in wide sense in the enhancement nodes. Model construction and learning algorithms are introduced here. Moreover, different approaches for the construction of enhancement nodes are given. The advantage of the Broad Learning System is that the learning can be updated dynamically and incrementally without going through a retraining process if the model deems to be expanded on additional feature nodes and enhancement nodes such that the learning is so efficient and effective. In addition, The incremental learning algorithms can be conveniently implemented for fast remodeling in broad expansion which can be referred in [1]. Compared with existing deep neural networks, experimental results on the MNIST data manifest the effectiveness of the Broad Learning System.

关键词： Single layer feedforward neural networks random vector functional link networks broad learning system deep learning

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