Multi-learner based recursive supervised training

Multi-learner based recursive supervised training

In this paper, we propose the multi-learner based recursive supervised training (MLRT) algorithm, which uses the existing framework of recursive task decomposition, by training the entire dataset, picking out the best learnt patterns, and then repeating the process with the remaining patterns. Inste...

Saved in:

書目詳細資料
Main Authors:	IYER, Laxmi R., RAMANATHAN, Kiruthika, GUAN, Sheng-Uei
格式:	text
語言:	English
出版:	Institutional Knowledge at Singapore Management University 2006
主題:	Neural Networks Supervised Learning Probabilistic Neural Networks (PNN) Backpropagation Databases and Information Systems OS and Networks
在線閱讀:	https://ink.library.smu.edu.sg/sis_research/7396 https://ink.library.smu.edu.sg/context/sis_research/article/8399/viewcontent/Multi_Learner_based_Recursive_Supervised_Training__1_.pdf
標簽:	添加標簽沒有標簽, 成為第一個標記此記錄!

相似書籍

MultiLearner based recursive supervised training
由: RAMANATHAN, Kiruthika, et al.
出版: (2006)

Multi-learner based recursive supervised training
由: IYER, Laxmi R., et al.
出版: (2006)

MultiLearner based recursive supervised training
由: Ramanathan, K., et al.
出版: (2014)

Multi-order Neurons for evolutionary higher order clustering and growth
由: RAMANATHAN, Kiruthika, et al.
出版: (2007)

Clustering and combinatorial optimization in recursive supervised learning
由: RAMANATHAN, Kiruthika, et al.
出版: (2007)

Classification of freeway traffic patterns for incident detection using constructive probabilistic neural networks
由: Jin, X., et al.
出版: (2014)

Evaluation of Adaptive Neural Network Models for Freeway Incident Detection
由: Srinivasan, D., et al.
出版: (2014)

Recursive pattern based hybrid training
由: KIRUTHIKA RAMANATHAN
出版: (2010)

Clustering and combinatorial optimization in recursive supervised learning
由: Ramanathan, K., et al.
出版: (2014)

Pruning meta-trained networks for on-device adaptation
由: GAO, Dawei, et al.
出版: (2021)

Symbolic rule extraction from neural networks An application to identifying organizations adopting IT
由: Setiono, R., et al.
出版: (2014)

Learning to pre-train graph neural networks
由: LU, Yuanfu, et al.
出版: (2021)

Prediction of protein-ligand binding affinity using neural networks
由: PAVANDIP SINGH WASAN
出版: (2010)

Improving backpropagation learning with feature selection
由: Setiono, R., et al.
出版: (2014)

Adaptive neural network models for automatic incident detection on freeways
由: Srinivasan, D., et al.
出版: (2014)

A neural network construction algorithm with application to image compression
由: Setiono, R., et al.
出版: (2014)

Causality analysis for neural network security
由: SUN, Bing
出版: (2024)

Incremental neural network training with an increasing input dimension
由: Guan, S.-U., et al.
出版: (2014)

Backpropagation neural networks training for single trial EEG classification
由: Turnip, A., et al.
出版: (2014)

Incremental learning in terms of output attributes
由: Guan, S.-U., et al.
出版: (2014)

Analysis of Hidden Representations by Greedy Clustering
由: Setiono, R., et al.
出版: (2014)

Recursive pattern based hybrid supervised training
由: RAMANATHAN, Kiruthika, et al.
出版: (2008)

Neural-network feature selector
由: Setiono, R., et al.
出版: (2014)

A Hubel Wiesel model of early concept generalization based on local correlation of input features
由: SADEGHI, Sepideh, et al.
出版: (2011)

DeepIS: Susceptibility estimation on social networks
由: XIA, Wenwen, et al.
出版: (2021)

Recent advances in deep learning for object detection
由: WU, Xiongwei, et al.
出版: (2020)

Probabilistic guided exploration for reinforcement learning in self-organizing neural networks
由: WANG, Peng, et al.
出版: (2018)

An incremental approach to contribution - based feature selection
由: LIU JUN
出版: (2011)

Adaptive resonance associative map
由: TAN, Ah-hwee
出版: (1995)

Technical Q8A site answer recommendation via question boosting
由: GAO, Zhipeng, et al.
出版: (2021)

On-device deep multi-task inference via multi-task zipping
由: HE, Xiaoxi, et al.
出版: (2023)

Surface roughness modelling with neural networks
由: PATRIKAR, Rajendra M., et al.
出版: (2002)

Evolutionary combinatorial optimization for recursive supervised learning with clustering
由: RAMANATHAN, Kiruthika, et al.
出版: (2007)

Toward deep supervised anomaly detection: Reinforcement learning from partially labeled anomaly data
由: PANG, Guansong, et al.
出版: (2021)

RNTrajRec: Road network enhanced trajectory recovery with spatial-temporal trans-former
由: CHEN, Yuqi, et al.
出版: (2023)

Deep anomaly detection with deviation networks
由: PANG, Guansong, et al.
出版: (2019)

Parallel nonlinear optimization techniques for training neural networks
由: Phua, P.K.H., et al.
出版: (2013)

Synthesis of annotated images as dataset for vehicle counting neural networks using semi-supervised learning
由: Chan, Patrick Matthew J.
出版: (2022)

Integrating self-organizing neural network and motivated learning for coordinated multi-agent reinforcement learning in multi-stage stochastic game
由: TENG, Teck-Hou, et al.
出版: (2014)

Learning to Self-Train for Semi-Supervised Few-Shot Classification
由: Xinzhe Li, et al.
出版: (2020)