Many regression algorithms, one unified model: A review

[1]  H. Nkansah Least squares optimization with L1-norm regularization , 2017 .

[2]  Jürgen Schmidhuber,et al.  Deep learning in neural networks: An overview , 2014, Neural Networks.

[3]  Surya Ganguli,et al.  Identifying and attacking the saddle point problem in high-dimensional non-convex optimization , 2014, NIPS.

[4]  Olivier Sigaud,et al.  Non-linear regression algorithms for motor skill acquisition: a comparison , 2014 .

[5]  Stefan Schaal,et al.  Local Gaussian Regression , 2014, ArXiv.

[6]  Razvan Pascanu,et al.  On the number of inference regions of deep feed forward networks with piece-wise linear activations , 2013, ICLR.

[7]  Yoshua Bengio,et al.  What regularized auto-encoders learn from the data-generating distribution , 2012, J. Mach. Learn. Res..

[8]  Razvan Pascanu,et al.  On the number of response regions of deep feed forward networks with piece-wise linear activations , 2013, 1312.6098.

[9]  Giorgio Metta,et al.  Real-time model learning using Incremental Sparse Spectrum Gaussian Process Regression. , 2013, Neural networks : the official journal of the International Neural Network Society.

[10]  Christopher M. Bishop,et al.  Model-based machine learning , 2013, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[11]  Jun Nakanishi,et al.  Dynamical Movement Primitives: Learning Attractor Models for Motor Behaviors , 2013, Neural Computation.

[12]  Pascal Vincent,et al.  Representation Learning: A Review and New Perspectives , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[13]  Bernard Widrow,et al.  The No-Prop algorithm: A new learning algorithm for multilayer neural networks , 2013, Neural Networks.

[14]  Olivier Sigaud,et al.  Autonomous online learning of velocity kinematics on the iCub: A comparative study , 2012, 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[15]  Stefan Schaal,et al.  Reinforcement Learning With Sequences of Motion Primitives for Robust Manipulation , 2012, IEEE Transactions on Robotics.

[16]  Martin V. Butz,et al.  Learning velocity kinematics: Experimental comparison of on-line regression algorithms , 2012 .

[17]  Olivier Sigaud,et al.  On-line regression algorithms for learning mechanical models of robots: A survey , 2011, Robotics Auton. Syst..

[18]  Lionel Rigoux,et al.  Learning cost-efficient control policies with XCSF: generalization capabilities and further improvement , 2011, GECCO '11.

[19]  Dianhui Wang,et al.  Extreme learning machines: a survey , 2011, Int. J. Mach. Learn. Cybern..

[20]  Giorgio Metta,et al.  Incremental learning of robot dynamics using random features , 2011, 2011 IEEE International Conference on Robotics and Automation.

[21]  Wei-Yin Loh,et al.  Classification and regression trees , 2011, WIREs Data Mining Knowl. Discov..

[22]  Pierre-Yves Oudeyer,et al.  Incremental local online Gaussian Mixture Regression for imitation learning of multiple tasks , 2010, 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[23]  Louis Goldstein,et al.  Locally-weighted regression for estimating the forward kinematics of a geometric vocal tract model , 2010, INTERSPEECH.

[24]  Martin V. Butz,et al.  Learning sensorimotor control structures with XCSF: redundancy exploitation and dynamic control , 2009, GECCO '09.

[25]  Jan Peters,et al.  Model Learning with Local Gaussian Process Regression , 2009, Adv. Robotics.

[26]  Carl E. Rasmussen,et al.  Gaussian processes for machine learning , 2005, Adaptive computation and machine learning.

[27]  Henk Nijmeijer,et al.  Robot Programming by Demonstration , 2010, SIMPAR.

[28]  Aude Billard,et al.  Dynamical System Modulation for Robot Learning via Kinesthetic Demonstrations , 2008, IEEE Transactions on Robotics.

[29]  Lei Chen,et al.  Enhanced random search based incremental extreme learning machine , 2008, Neurocomputing.

[30]  Martin V. Butz,et al.  Context-dependent predictions and cognitive arm control with XCSF , 2008, GECCO '08.

[31]  Daniel H. Grollman,et al.  Sparse incremental learning for interactive robot control policy estimation , 2008, 2008 IEEE International Conference on Robotics and Automation.

[32]  Michael Beetz,et al.  Refining the Execution of Abstract Actions with Learned Action Models , 2008, J. Artif. Intell. Res..

[33]  Mark Ebden Gaussian Processes for Regression: A Quick Introduction , 2008 .

[34]  AI Koan,et al.  Weighted Sums of Random Kitchen Sinks: Replacing minimization with randomization in learning , 2008, NIPS.

[35]  Chee Kheong Siew,et al.  Incremental extreme learning machine with fully complex hidden nodes , 2008, Neurocomputing.

[36]  Benjamin Recht,et al.  Random Features for Large-Scale Kernel Machines , 2007, NIPS.

[37]  Aude Billard,et al.  On Learning, Representing, and Generalizing a Task in a Humanoid Robot , 2007, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[38]  Nasser M. Nasrabadi,et al.  Pattern Recognition and Machine Learning , 2006, Technometrics.

[39]  Yoshua. Bengio,et al.  Learning Deep Architectures for AI , 2007, Found. Trends Mach. Learn..

[40]  Chee Kheong Siew,et al.  Extreme learning machine: Theory and applications , 2006, Neurocomputing.

[41]  Christopher M. Bishop,et al.  Pattern Recognition and Machine Learning (Information Science and Statistics) , 2006 .

[42]  Chee Kheong Siew,et al.  Universal Approximation using Incremental Constructive Feedforward Networks with Random Hidden Nodes , 2006, IEEE Transactions on Neural Networks.

[43]  Carl E. Rasmussen,et al.  A Unifying View of Sparse Approximate Gaussian Process Regression , 2005, J. Mach. Learn. Res..

[44]  Bernhard Schölkopf,et al.  A tutorial on support vector regression , 2004, Stat. Comput..

[45]  Marijke F. Augusteijn,et al.  Evolving transfer functions for artificial neural networks , 2003, Neural Computing & Applications.

[46]  Tony R. Martinez,et al.  The general inefficiency of batch training for gradient descent learning , 2003, Neural Networks.

[47]  Nathan Intrator,et al.  A Hybrid Projection Based and Radial Basis Function Architecture , 2000, Multiple Classifier Systems.

[48]  Michael Schmitt,et al.  On the Complexity of Computing and Learning with Multiplicative Neural Networks , 2002, Neural Computation.

[49]  Friedhelm Schwenker,et al.  Three learning phases for radial-basis-function networks , 2001, Neural Networks.

[50]  Yoshua Bengio,et al.  Gradient Flow in Recurrent Nets: the Difficulty of Learning Long-Term Dependencies , 2001 .

[51]  John Hallam,et al.  Combining Regression Trees and Radial Basis Function Networks , 2000, Int. J. Neural Syst..

[52]  Bernhard Schölkopf,et al.  New Support Vector Algorithms , 2000, Neural Computation.

[53]  Stefan Schaal,et al.  Locally Weighted Projection Regression : An O(n) Algorithm for Incremental Real Time Learning in High Dimensional Space , 2000 .

[54]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[55]  Alexander Gammerman,et al.  Ridge Regression Learning Algorithm in Dual Variables , 1998, ICML.

[56]  Christopher K. I. Williams Computation with Infinite Neural Networks , 1998, Neural Computation.

[57]  Vladimir Cherkassky,et al.  The Nature Of Statistical Learning Theory , 1997, IEEE Trans. Neural Networks.

[58]  Stefan Schaal,et al.  Receptive Field Weighted Regression , 1997 .

[59]  Ian H. Witten,et al.  Induction of model trees for predicting continuous classes , 1996 .

[60]  R. Tibshirani Regression Shrinkage and Selection via the Lasso , 1996 .

[61]  Stefan Schaal,et al.  Memory-based neural networks for robot learning , 1995, Neurocomputing.

[62]  Christopher M. Bishop,et al.  Neural networks for pattern recognition , 1995 .

[63]  Geoffrey E. Hinton,et al.  Bayesian Learning for Neural Networks , 1995 .

[64]  G. Dorffner UNIFIED FRAMEWORK FOR MLPs AND RBFNs: INTRODUCING CONIC SECTION FUNCTION NETWORKS , 1994 .

[65]  Michael I. Jordan,et al.  Supervised learning from incomplete data via an EM approach , 1993, NIPS.

[66]  Jooyoung Park,et al.  Approximation and Radial-Basis-Function Networks , 1993, Neural Computation.

[67]  J. R. Quinlan Learning With Continuous Classes , 1992 .

[68]  F. Girosi,et al.  Networks for approximation and learning , 1990, Proc. IEEE.

[69]  PAUL J. WERBOS,et al.  Generalization of backpropagation with application to a recurrent gas market model , 1988, Neural Networks.

[70]  Robin Sibson,et al.  What is projection pursuit , 1987 .

[71]  B. Kowalski,et al.  Partial least-squares regression: a tutorial , 1986 .

[72]  J. Friedman,et al.  Projection Pursuit Regression , 1981 .

[73]  W. Steiger,et al.  Least Absolute Deviations Curve-Fitting , 1980 .

[74]  D. Rubin,et al.  Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .

[75]  P. Werbos,et al.  Beyond Regression : "New Tools for Prediction and Analysis in the Behavioral Sciences , 1974 .

[76]  F ROSENBLATT,et al.  The perceptron: a probabilistic model for information storage and organization in the brain. , 1958, Psychological review.

[77]  R. Plackett Some theorems in least squares. , 1950, Biometrika.

[78]  M. Kendall Statistical Methods for Research Workers , 1937, Nature.