Broad Learning System: An Effective and Efficient Incremental Learning System Without the Need for Deep Architecture

By C. L. Philip Chen, Zhulin Liu. IEEE Transactions on Neural Networks and Learning Systems, 2018

1,615 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/f2c5a5b90a4c271a783e97af7427db647ca5b9f9

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Citations in the corpus

A Review of Fault Detection and Diagnosis for the Traction System in High-Speed Trains (2020, methodology): “1: Preprocess Zς based on step 1 in Algorithm 1 as Zς ; 2: Project Zς into obtained principal and residual subspaces in step 2 in Algorithm 1 as T̂ς and T̃ς ; 3: Define Tς = [T̂ς T̃ς ] as the mapped feature nodes of Zς ; 4: Obtain the enhancement nodes Hς of Zς [126]; 5: Output the ς -th type of fau”
Adaptive Neural Network Control of a Flapping Wing Micro Aerial Vehicle With Disturbance Observer (2017, background): “We need to further design more appropriate controllers to realize ultimate objects on an ongoing current basis [15]–[21].”
Universal Approximation Capability of Broad Learning System and Its Structural Variations (2019, methodology): “Hence, only the increment of the additional enhancement nodes is available here, and the algorithm is similar to the corresponding section of the original BLS in [3].”
NPENAS: Neural Predictor Guided Evolution for Neural Architecture Search (2022, methodology): “Besides the above search strategies, the extreme learning machine (ELM) [37], [38] and broad learning system (BLS) [39] can also be used to design the structure of neural networks automatically.”
KGSNet: Key-Point-Guided Super-Resolution Network for Pedestrian Detection in the Wild (2020, background): “With the development of deep convolutional neural networks (DCNNs) [11]–[15], pedestrian detection has been extensively studied and huge progress has been made over the past few years.”
Memory-Efficient Class-Incremental Learning for Image Classification (2020, neutral): “Recently, there have been a lot of research works on incremental learning with deep models [33]–[46].”
Underwater Internet of Things in Smart Ocean: System Architecture and Open Issues (2020, background): “can learn network state and environment parameters adaptively, such as reinforcement learning [57], broad learning [58], deep learning [59], decision tree [60], and self-organizing map [61].”
Fuzzy Broad Learning System: A Novel Neuro-Fuzzy Model for Regression and Classification (2020, background): “BLS [28] is an improved flat framework [29] which inherits the major feature and superiority of a random vector functional-link NN [30].”
IncDet: In Defense of Elastic Weight Consolidation for Incremental Object Detection (2020, background): “Broad learning system (BLS) [36], [37] develops incremental learning algorithms for increment of input variables [38], feature nodes [39] and enhancement nodes.”