Intuition Learning

“By reading on the title and abstract, do you think this paper will be accepted in IJCAI 2018?” A common impromptu reply would be “I don’t know but I have an intuition that this paper might get accepted”. Intuition is often employed by humans to solve challenging problems without explicit efforts. Intuition is not trained but is learned from one’s own experience and observation. The aim of this research is to provide intuition to an algorithm, apart from what they are trained to know in a supervised manner. We present a novel intuition learning framework that learns to perform a task completely from unlabeled data. The proposed framework uses a continuous-state reinforcement learning mechanism to learn a feature representation and a data-label mapping function using unlabeled data. The mapping functions and feature representation are succinct and can be used to supplement any supervised or semi-supervised algorithm. The experiments on the CIFAR-10 database shows challenging cases where intuition learning improve the performance of a given classifier.

[1]  Lihong Li,et al.  Unbiased online active learning in data streams , 2011, KDD.

[2]  Antonio Torralba,et al.  Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope , 2001, International Journal of Computer Vision.

[3]  Valerie F. Reyna,et al.  Educating Intuition , 2015, Current directions in psychological science.

[4]  T. Michael Knasel,et al.  Robotics and autonomous systems , 1988, Robotics Auton. Syst..

[5]  Honglak Lee,et al.  An Analysis of Single-Layer Networks in Unsupervised Feature Learning , 2011, AISTATS.

[6]  Kristian Kersting,et al.  Imitation Learning in Relational Domains: A Functional-Gradient Boosting Approach , 2011, IJCAI.

[7]  Joost van de Weijer,et al.  Robust photometric invariant features from the color tensor , 2006, IEEE Transactions on Image Processing.

[8]  A. Karimi,et al.  Master‟s thesis , 2011 .

[9]  Leemon C. Baird,et al.  Residual Algorithms: Reinforcement Learning with Function Approximation , 1995, ICML.

[10]  Jing Huang,et al.  Image indexing using color correlograms , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[11]  Andrew W. Moore,et al.  Reinforcement Learning: A Survey , 1996, J. Artif. Intell. Res..

[12]  P. Cochat,et al.  Et al , 2008, Archives de pediatrie : organe officiel de la Societe francaise de pediatrie.

[13]  Bernhard Schölkopf,et al.  Correcting Sample Selection Bias by Unlabeled Data , 2006, NIPS.

[14]  A. Sayed,et al.  Foundations and Trends ® in Machine Learning > Vol 7 > Issue 4-5 Ordering Info About Us Alerts Contact Help Log in Adaptation , Learning , and Optimization over Networks , 2011 .

[15]  Rajat Raina,et al.  Self-taught learning: transfer learning from unlabeled data , 2007, ICML '07.

[16]  Neil D. Lawrence,et al.  Learning to learn with the informative vector machine , 2004, ICML.

[17]  Robert Sabourin,et al.  LoGID: An adaptive framework combining local and global incremental learning for dynamic selection of ensembles of HMMs , 2012, Pattern Recognit..

[18]  Richard S. Sutton,et al.  Reinforcement Learning: An Introduction , 1998, IEEE Trans. Neural Networks.