Ensemble of classification models with weighted functional link network
暂无分享,去创建一个
[1] Rui Guo,et al. A Twin Multi-Class Classification Support Vector Machine , 2012, Cognitive Computation.
[2] Dirk Husmeier,et al. Neural Networks for Predicting Conditional Probability Densities: Improved Training Scheme Combining EM and RVFL , 1998, Neural Networks.
[3] P. N. Suganthan,et al. Regularized robust fuzzy least squares twin support vector machine for class imbalance learning , 2020, 2020 International Joint Conference on Neural Networks (IJCNN).
[4] Ponnuthurai N. Suganthan,et al. Random vector functional link network for short-term electricity load demand forecasting , 2016, Inf. Sci..
[5] Juan José Rodríguez Diez,et al. Classifier Ensembles with a Random Linear Oracle , 2007, IEEE Transactions on Knowledge and Data Engineering.
[6] Ponnuthurai N. Suganthan,et al. Minimum Variance Embedded Random Vector Functional Link Network with Privileged Information , 2022, 2022 International Joint Conference on Neural Networks (IJCNN).
[7] Muhammad Tanveer,et al. Robust energy-based least squares twin support vector machines , 2015, Applied Intelligence.
[8] P. N. Suganthan,et al. Random Vector Functional Link Neural Network based Ensemble Deep Learning , 2019, Pattern Recognit..
[9] Muhammad Tanveer. Robust and Sparse Linear Programming Twin Support Vector Machines , 2014, Cognitive Computation.
[10] Ponnuthurai Nagaratnam Suganthan,et al. Oblique Decision Tree Ensemble via Twin Bounded SVM , 2020, Expert Syst. Appl..
[11] Dejan J. Sobajic,et al. Neural-net computing and the intelligent control of systems , 1992 .
[12] Ponnuthurai N. Suganthan,et al. General twin support vector machine with pinball loss function , 2019, Inf. Sci..
[13] M. Tanveer,et al. Sparse Twin Support Vector Clustering Using Pinball Loss , 2021, IEEE Journal of Biomedical and Health Informatics.
[14] Ponnuthurai N. Suganthan,et al. Random Forests with ensemble of feature spaces , 2014, Pattern Recognit..
[15] Ponnuthurai Nagaratnam Suganthan,et al. Least squares KNN-based weighted multiclass twin SVM , 2020, Neurocomputing.
[16] Ivan Tyukin,et al. Feasibility of random basis function approximators for modeling and control , 2009, 2009 IEEE Control Applications, (CCA) & Intelligent Control, (ISIC).
[17] Peng-Bo Zhang,et al. A New Learning Paradigm for Random Vector Functional-Link Network: RVFL+ , 2017, Neural Networks.
[18] Philip S. Yu,et al. An unsupervised parameter learning model for RVFL neural network , 2019, Neural Networks.
[19] Senén Barro,et al. Do we need hundreds of classifiers to solve real world classification problems? , 2014, J. Mach. Learn. Res..
[20] Ponnuthurai Nagaratnam Suganthan,et al. Comprehensive evaluation of twin SVM based classifiers on UCI datasets , 2019, Appl. Soft Comput..
[21] Jalal A. Nasiri,et al. Least squares twin multi-class classification support vector machine , 2015, Pattern Recognit..
[22] M. A. Ganaie,et al. Robust General Twin Support Vector Machine with Pinball Loss Function , 2021 .
[23] Jalal A. Nasiri,et al. Energy-based model of least squares twin Support Vector Machines for human action recognition , 2014, Signal Process..
[24] P. N. Suganthan,et al. A comprehensive evaluation of random vector functional link networks , 2016, Inf. Sci..
[25] Chunhua Zhang,et al. The new interpretation of support vector machines on statistical learning theory , 2010 .
[26] M. A. Ganaie,et al. Improved Sparse Pinball Twin SVM , 2019, 2019 IEEE International Conference on Systems, Man and Cybernetics (SMC).
[27] Muhammad Tanveer,et al. Sparse pinball twin support vector machines , 2019, Appl. Soft Comput..
[28] Leo Breiman,et al. Random Forests , 2001, Machine Learning.
[29] Yee Whye Teh,et al. A Fast Learning Algorithm for Deep Belief Nets , 2006, Neural Computation.
[30] Dejan J. Sobajic,et al. Learning and generalization characteristics of the random vector Functional-link net , 1994, Neurocomputing.
[31] S. R. LeClair,et al. Intelligent rate control for MPEG-4 coders , 2000 .
[32] Yuan-Hai Shao,et al. Improvements on Twin Support Vector Machines , 2011, IEEE Transactions on Neural Networks.
[33] Yoh-Han Pao,et al. The functional link net and learning optimal control , 1995, Neurocomputing.
[34] Geoffrey E. Hinton,et al. Reducing the Dimensionality of Data with Neural Networks , 2006, Science.
[35] Shan Juan Xie,et al. A High Accuracy Pedestrian Detection System Combining a Cascade AdaBoost Detector and Random Vector Functional-Link Net , 2014, TheScientificWorldJournal.
[36] Janez Demsar,et al. Statistical Comparisons of Classifiers over Multiple Data Sets , 2006, J. Mach. Learn. Res..
[37] C. L. Philip Chen,et al. A rapid learning and dynamic stepwise updating algorithm for flat neural networks and the application to time-series prediction , 1999, IEEE Trans. Syst. Man Cybern. Part B.
[38] Muhammad Tanveer,et al. LSTSVM classifier with enhanced features from pre-trained functional link network , 2020, Appl. Soft Comput..
[39] Okan K. Ersoy,et al. A statistical self-organizing learning system for remote sensing classification , 2005, IEEE Transactions on Geoscience and Remote Sensing.
[40] Madan Gopal,et al. Least squares twin support vector machines for pattern classification , 2009, Expert Syst. Appl..
[41] Manuel Fernández Delgado,et al. Exhaustive comparison of colour texture features and classification methods to discriminate cells categories in histological images of fish ovary , 2013, Pattern Recognit..
[42] Yoh-Han Pao,et al. Stochastic choice of basis functions in adaptive function approximation and the functional-link net , 1995, IEEE Trans. Neural Networks.
[43] Yoh-Han Pao,et al. Unconstrained word-based approach for off-line script recognition using density-based random-vector functional-link net , 2000, Neurocomputing.