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Recursive lazy learning for modeling and control
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Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1002/bimj.19730150510openalex-snapshot
JACOBSON, D. H. and D. Q. MAYNE : Differential dynamic programming. Modern analytic and computational methods in Science and Mathematics, No. 24. American Elsevier Publ. Co., Inc., New York 1970. XVI, 208 S., 17 Abb., Dfl. 51.50.
inferredcites →https://doi.org/10.1007/978-1-4899-4541-9openalex-snapshot
An Introduction to the Bootstrap
inferredcites →https://doi.org/10.1007/springerreference_117344openalex-snapshot
Neural Networks for Control
inferredcites →https://doi.org/10.1016/0005-1098(85)90014-7openalex-snapshot
Adaptive filtering, prediction and control
inferredcites →https://doi.org/10.1016/b978-1-55860-036-2.50098-9openalex-snapshot
INCREMENTAL, INSTANCE-BASED LEARNING OF INDEPENDENT AND GRADED CONCEPT DESCRIPTIONS
inferredcites →https://doi.org/10.1016/s0076-5392(08)x6056-4openalex-snapshot
Optimal Control Systems
inferredcites →https://doi.org/10.1016/s0898-1221(96)90087-5openalex-snapshot
Empirical methods for artificial intelligence
inferredcites →https://doi.org/10.1023/a:1006511328852openalex-snapshot
Locally Weighted Learning for Control
inferredcites →https://doi.org/10.1080/00207179308923046openalex-snapshot
Constructing NARMAX models using ARMAX models
inferredcites →https://doi.org/10.1080/0020718508961129openalex-snapshot
Input-output parametric models for non-linear systems Part I: deterministic non-linear systems
inferredcites →https://doi.org/10.1080/0020718508961130openalex-snapshot
Input-output parametric models for non-linear systems Part II: stochastic non-linear systems
inferredcites →https://doi.org/10.1093/oso/9780198562924.001.0001openalex-snapshot
Advances in Model-Based Predictive Control
inferredcites →https://doi.org/10.1109/37.165527openalex-snapshot
Gain scheduling: potential hazards and possible remedies
inferredcites →https://doi.org/10.1109/72.557698openalex-snapshot
A simplification of the backpropagation-through-time algorithm for optimal neurocontrol
inferredcites →https://doi.org/10.1109/91.481952openalex-snapshot
Stability and stabilizability of fuzzy-neural-linear control systems
inferredcites →https://doi.org/10.1109/cdc.1992.371803openalex-snapshot
Neural networks in control systems
inferredcites →https://doi.org/10.1109/ijcnn.1998.687238openalex-snapshot
Adaptive memory based regression methods
inferredcites →https://doi.org/10.1109/tac.1971.1099760openalex-snapshot
Differential dynamic programming
inferredcites →https://doi.org/10.1109/tsmc.1985.6313399openalex-snapshot
Fuzzy identification of systems and its applications to modeling and control
inferredcites →https://doi.org/10.1111/j.2517-6161.1977.tb01622.xopenalex-snapshot
On the Nonparametric Estimation of Regression Functions
inferredcites →https://doi.org/10.1137/1114019openalex-snapshot
Non-Parametric Estimation of a Multivariate Probability Density
inferredcites →https://doi.org/10.1201/9780429246593openalex-snapshot
An Introduction to the Bootstrap
inferredcites →https://doi.org/10.2307/1269070openalex-snapshot
Classical and Modern Regression with Applications
inferredcites →https://doi.org/10.2307/2288958openalex-snapshot
Classical and Modern Regression With Applications.
inferredcites →https://doi.org/10.2307/2551606openalex-snapshot
Non-Parametric Statistics for the Behavioral Sciences.
inferredcites →https://doi.org/10.2307/3498751openalex-snapshot
Nonparametric Statistics for the Behavioral Sciences
inferredcites →https://doi.org/10.5860/choice.33-5144openalex-snapshot
Empirical methods for artificial intelligence
inferredcites →https://doi.org/10.7551/mitpress/4939.001.0001openalex-snapshot
Neural Networks for Control
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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OP-ELM: Theory, Experiments and a Toolbox
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Adaptive Ensemble Models of Extreme Learning Machines for Time Series Prediction
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Lazy Learning: A Logical Method for Supervised Learning
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Neural-empirical tyre model based on recursive lazy learning under combined longitudinal and lateral slip conditions
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A Two-Stage Methodology Using K-NN and False-Positive Minimizing ELM for Nominal Data Classification
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Integration of hydrometric upstream runoff and Optimal Pruned Extreme Learning Machine for real-time flood forecasting
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Comparing lazy and eager learning models for water level forecasting in river-reservoir basins of inundation regions
inferred← citeshttps://doi.org/10.1016/j.eswa.2007.08.047openalex-snapshot
A new training method for support vector machines: Clustering k-NN support vector machines
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Modeling of a magnetorheological damper by recursive lazy learning
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Recent advances in neuro-fuzzy system: A survey
inferred← citeshttps://doi.org/10.1016/j.neucom.2009.11.033openalex-snapshot
OPELM and OPKNN in long-term prediction of time series using projected input data
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Two-stage extreme learning machine for regression
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Regularized extreme learning machine for regression with missing data
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Bankruptcy prediction using Extreme Learning Machine and financial expertise
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Parsimonious regularized extreme learning machine based on orthogonal transformation
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The local paradigm for modeling and control: from neuro-fuzzy to lazy learning
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Research into Financial Position of Listed Companies following Classification via Extreme Learning Machine Based upon DE Optimization
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A support vector machine using the lazy learning approach for multi-class classification
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Predicting stock markets in boundary conditions with local models
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A Novel Lazy Learning-based Data Driven Fault Diagnosis Method for Public Transport Vehicles
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Optimal Pruned K-Nearest Neighbors: OP-KNN Application to Financial Modeling
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Causality tests using basic and optimally-pruned extreme learning machines
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Electricity Load Forecasting Using an Ensemble of Optimally-Pruned and Basic Extreme Learning Machines
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Long-term prediction of time series using NNE-based projection and OP-ELM
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A fast and effective Extreme learning machine algorithm without tuning
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Conditional Training Based GM and GM-OPELM Data Fusion Schemes in Wireless Sensor Networks
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OP-ELM: Optimally Pruned Extreme Learning Machine
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Protein Sequence Classification with Improved Extreme Learning Machine Algorithms
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Network ensemble and constructive algorithms for model selection of extreme learning machine
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Spectrum Prediction for Cognitive Radio System Based on Optimally Pruned Extreme Learning Machine
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