Representation learning using deep random vector functional link networks for clustering
暂无分享,去创建一个
[1] Shaoyi Du,et al. Hypergraph Learning: Methods and Practices , 2020, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[2] Laurent Jacques,et al. The Separation Capacity of Random Neural Networks , 2021, ArXiv.
[3] Ponnuthurai N. Suganthan,et al. On the origins of randomization-based feedforward neural networks , 2021, Appl. Soft Comput..
[4] P. N. Suganthan,et al. Random Vector Functional Link Neural Network based Ensemble Deep Learning , 2019, Pattern Recognit..
[5] Pradipta Kishore Dash,et al. Real-time Energy Management for PV-battery-wind based microgrid using on-line sequential Kernel Based Robust Random Vector Functional Link Network , 2021, Applied Soft Computing.
[6] Barenya Bikash Hazarika,et al. Modelling and forecasting of COVID-19 spread using wavelet-coupled random vector functional link networks , 2020, Applied Soft Computing.
[7] Rayan Saab,et al. Random Vector Functional Link Networks for Function Approximation on Manifolds , 2020, Frontiers in Applied Mathematics and Statistics.
[8] Guang-Bin Huang,et al. Clustering via Adaptive and Locality-constrained Graph Learning and Unsupervised ELM , 2020, Neurocomputing.
[9] P. Suganthan,et al. Stacked Autoencoder Based Deep Random Vector Functional Link Neural Network for Classification , 2019, Appl. Soft Comput..
[10] Fakhri Karray,et al. Eigenvalue and Generalized Eigenvalue Problems: Tutorial , 2019, ArXiv.
[11] Zhiping Lin,et al. An adaptive graph learning method based on dual data representations for clustering , 2018, Pattern Recognit..
[12] Ponnuthurai N. Suganthan,et al. Ensemble incremental learning Random Vector Functional Link network for short-term electric load forecasting , 2018, Knowl. Based Syst..
[13] Le Zhang,et al. An ensemble of decision trees with random vector functional link networks for multi-class classification , 2017, Appl. Soft Comput..
[14] P. N. Suganthan,et al. Benchmarking Ensemble Classifiers with Novel Co-Trained Kernal Ridge Regression and Random Vector Functional Link Ensembles [Research Frontier] , 2017, IEEE Computational Intelligence Magazine.
[15] Feiping Nie,et al. The Constrained Laplacian Rank Algorithm for Graph-Based Clustering , 2016, AAAI.
[16] Feiping Nie,et al. Clustering and projected clustering with adaptive neighbors , 2014, KDD.
[17] Cheng Wu,et al. Semi-Supervised and Unsupervised Extreme Learning Machines , 2014, IEEE Transactions on Cybernetics.
[18] Emilio Corchado,et al. A survey of multiple classifier systems as hybrid systems , 2014, Inf. Fusion.
[19] Yong Yu,et al. Robust Recovery of Subspace Structures by Low-Rank Representation , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[20] Sandro Vega-Pons,et al. A Survey of Clustering Ensemble Algorithms , 2011, Int. J. Pattern Recognit. Artif. Intell..
[21] Mohamed S. Kamel,et al. On voting-based consensus of cluster ensembles , 2010, Pattern Recognit..
[22] Mohamed S. Kamel,et al. Cumulative Voting Consensus Method for Partitions with Variable Number of Clusters , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[23] Kagan Tumer,et al. Classifier ensembles: Select real-world applications , 2008, Inf. Fusion.
[24] Ulrike von Luxburg,et al. A tutorial on spectral clustering , 2007, Stat. Comput..
[25] Sergei Vassilvitskii,et al. k-means++: the advantages of careful seeding , 2007, SODA '07.
[26] Mikhail Belkin,et al. Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples , 2006, J. Mach. Learn. Res..
[27] Chee Kheong Siew,et al. Extreme learning machine: Theory and applications , 2006, Neurocomputing.
[28] Ludmila I. Kuncheva,et al. Moderate diversity for better cluster ensembles , 2006, Inf. Fusion.
[29] Ann B. Lee,et al. Geometric diffusions as a tool for harmonic analysis and structure definition of data: diffusion maps. , 2005, Proceedings of the National Academy of Sciences of the United States of America.
[30] Ulrike von Luxburg,et al. Limits of Spectral Clustering , 2004, NIPS.
[31] William F. Punch,et al. A Comparison of Resampling Methods for Clustering Ensembles , 2004, IC-AI.
[32] Mikhail Belkin,et al. Laplacian Eigenmaps for Dimensionality Reduction and Data Representation , 2003, Neural Computation.
[33] D. Donoho,et al. Hessian eigenmaps: Locally linear embedding techniques for high-dimensional data , 2003, Proceedings of the National Academy of Sciences of the United States of America.
[34] Michael I. Jordan,et al. On Spectral Clustering: Analysis and an algorithm , 2001, NIPS.
[35] Mikhail Belkin,et al. Laplacian Eigenmaps and Spectral Techniques for Embedding and Clustering , 2001, NIPS.
[36] Dejan J. Sobajic,et al. Learning and generalization characteristics of the random vector Functional-link net , 1994, Neurocomputing.
[37] Andrew R. Barron,et al. Universal approximation bounds for superpositions of a sigmoidal function , 1993, IEEE Trans. Inf. Theory.
[38] Y. Takefuji,et al. Functional-link net computing: theory, system architecture, and functionalities , 1992, Computer.
[39] George Cybenko,et al. Approximation by superpositions of a sigmoidal function , 1989, Math. Control. Signals Syst..
[40] Kurt Hornik,et al. Multilayer feedforward networks are universal approximators , 1989, Neural Networks.
[41] J. Munkres. ALGORITHMS FOR THE ASSIGNMENT AND TRANSIORTATION tROBLEMS* , 1957 .