Machine learning is the self-driven technology. It is the science of getting computers to act without being explicitly programmed. Machine learning refers to self-improving algorithms, explores the study and construction of algorithms that can learn from and make predictions on data. These are predefined processes conforming to specific rules, performed by a computer can be applied to any learning task and it is flexible and it don’t need a programmer or human expert.Machine learning algorithms are common in web applications that we use every day and have a growing relevance to enterprise applications. Two of the most widely adopted machine learning methods are supervised learning and unsupervised learning. While many machine learning algorithms have been around for a long time, the ability to automatically apply complex mathematical calculations to big data – over and over, faster and faster is a recent development.

Data mining, Artificial Intelligence, Neural Networks and Machine learning

[1] Abdullahi Uwaisu Muhammad, Abdullahi Garba Musa, Kamaluddeen Ibrahim Yarima,” Survey on Training Neural Networks “, International Journal of Advanced Research in Computer Science and Software Engineering.
[2] Anish Talwar, Yogesh Kumar,” Machine Learning: An artificial intelligence methodology”, International Journal Of Engineering And Computer Science ISSN:23197242.
[3] Bora Gaze,Steven Minton,” Overview of AutoFeed: An Unsupervised Learning System for GeneratingWebfeeds”, Fetch Technologies 2041 Rosecrans Ave.El Segundo, California, USA.
[4] S.Balaji, Dr.S.K.Srivatsa,” Unsupervised Learning in Large Datasets for Intelligent Decision Making”, International Journal of Scientific and Research Publications, Volume 2, Issue 9, September 2012 1 ISSN 2250-3153.
[5] S.R.K. Branavan, Harr Chen, Luke S. Zettlemoyer, Regina Barzilay,” Reinforcement Learning for Mapping Instructions to Actions”, Computer Science and Artificial Intelligence Laboratory.
[6] Carlos Diuk, Andre Cohen, Michael L. Littman,” An Object-Oriented Representation for Efficient Reinforcement Learning”, RL3 Laboratory, Department of Computer Science, Rutgers University, Piscataway, NJ USA.
[7] Charles Mathy, Nate Derbinsky, Jose Bento, Jonathan Rosenthal, Jonathan Yedidia,” The Boundary Forest Algorithm for Online Supervised and Unsupervised Learning”, Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence.
[8] Dasika Ratna Deepthi, G.R.Aditya Krishna, K. Eswaran,”Automatic pattern classification by unsupervised learning using dimensionality reduction of data with mirroring neural networks”.
[9] R.Deepa Lakshmi, N.Radha,” Supervised Learning Approach for Spam Classification Analysis using Data Mining Tools”, (IJCSE) International Journal on Computer Science and Engineering Vol. 02, No. 08, 2010, 2760-2766.
[10] Gao Huang, Shiji Song, Jatinder N. D. Gupta, and Cheng Wu,” Semi-supervised and unsupervised extreme learning machines”, IEEE transactions on cybernetics.
[11] Gideon S. Mann, Andrew McCallum,” Simple, Robust, Scalable Semi-supervised Learning via Expectation Regularization”, Proceedings of the 24 th International Conference on Machine Learning, Corvallis, OR, 2007. Copyright 2007 by the author(s)/owner(s).
[12] Gilles Blanchard, Gyemin Lee Clayton Scott,” Semi-Supervised Novelty Detection”, Journal of Machine Learning Research 11 (2010) 2973-3009.
[13] Hetal Bhavsar, Amit Ganatra,” A Comparative Study of Training Algorithms for Supervised Machine Learning”, International Journal of Soft Computing and Engineering (IJSCE) ISSN: 2231-2307, Volume-2, Issue-4, September 2012.
[14] Iqbal Muhammad, Zhu Yan,” Supervised machine learning approaches: a survey”, ictact journal on soft computing, april 2015, volume: 05, issue: 03.
[15] Jennifer G. Dy, Carla E. Brodley,” Feature Selection for Unsupervised Learning”, Journal of Machine Learning Research 5 (2004) 845–889.
[16] Jens Kober, J. Andrew Bagnell, Jan Peters,” Reinforcement Learning in Robotics: A Survey”, Kober IJRR 2013.
[17] Junhui Wang, Xiaotong Shen, Wei Pan,” On Efficient Large Margin Semisupervised Learning: Method and Theory”, Journal of Machine Learning Research 10 (2009) 719-742.
[18] Koushal Kumar, Gour Sundar Mitra Thakur,” Advanced Applications of Neural Networks and Artificial Intelligence: A Review”, I.J. Information Technology and Computer Science, 2012, 6, 57-68.
[19] Krishnakumar Balasubramanian, Pinar Donmez, Guy Lebanon,” Unsupervised Supervised Learning II: Margin-Based Classification without Labels”, Proceedings of the 14th International Conference on Artificial Intelligence and Statistics (AISTATS) 2011, Fort Lauderdale, FL, USA. Volume 15 of JMLR: W&CP 15. Copyright 2011 by the authors.
[20] Lei Jimmy Ba, Brendan Frey,” Adaptive dropout for training deep neural networks”, Advances in Neural Information Processing Systems.
[21] Leslie Pack Kaelbling, Michael L. Littman, Andrew W. Moore,” Reinforcement Learning: A Survey”, Journal of Artificial Intelligence Research 4 (1996) 237-285.
[22] Manju.S, M.Punithavalli,”Neural network-based ideation learning for intelligent agents: e-brainstorming with privacy preferences”,International Journal of Computational Vision and Robotics,Vol. 5, No. 3, 2015.
[23] Manju.S, M. Punithavalli, “An Analysis of Q-Learning Algorithms with Strategies of Reward Function, International Journal on Computer Science and Engineering”,ISSN : 0975-3397 Vol. 3 No. 2 Feb 2011.
[24] Marc’Aurelio Ranzato, Fu-Jie Huang, Y-Lan Boureau, Yann LeCun,”Unsupervised Learning of Invariant Feature Hierarchies with Applications to Object Recognition”.
[25] Michał Kozielski, Malte Nuhn, Patrick Doetsch, Hermann Ney,” Towards Unsupervised Learning for Handwriting Recognition”, Human Language Technology and Pattern Recognition Group.
[26] Mykola Pechenizkiy, Alexey Tsymbal, and Seppo Puuronen,” Local Dimensionality Reduction and Supervised Learning Within Natural Clusters for Biomedical Data Analysis”,IEEE transactions on information technology in biomedicine, vol. 10, no. 3, july 2006.
[27] G. Nguyen, A. Bouzerdoum & S. Lam. Phung, "A supervised learning approach for imbalanced data sets," in International Conference on Pattern Recognition, 2008, pp. 1-4.
[28] Oliver Brdiczka, Patrick Reignier & James L. Crowley,” Supervised Learning of an Abstract Context Model for an Intelligent Environment”, Grenoble, october 2005 Joint sOc-EUSAI conference.
[29] Olivier Chapelle, Vikas Sindhwani, Sathiya S. Keerthi,” Optimization Techniques for Semi-Supervised Support Vector Machines”, Journal of Machine Learning Research 9 (2008) 203-233.
[30] Quoc V,Marc’Aurelio Ranzato,Rajat Monga,Matthieu Devin,Kai Chen,Greg S. Corrado,Jeff Dean,Andrew Y. Ng,” Building High-level Features Using Large Scale Unsupervised Learning”, Proceedings of the 29 th International Conference on Machine Learning, Edinburgh, Scotland, UK, 2012.
[31] Rich Caruana,Alexandru Niculescu-Mizil,” An Empirical Comparison of Supervised Learning Algorithms”, Proceedings of the 23 rd International Con-ference on Machine Learning, Pittsburgh, PA, 2006.
[32] Rie Kubota Ando, Tong Zhang,” A High-Performance Semi-Supervised Learning Method for Text Chunking”, IBM T.J. Watson Research Center Yorktown Heights, NY 10598, U.S.A.
[33] Rohit J. Kate and Raymond J. Mooney,” Semi-Supervised Learning for Semantic Parsing using Support Vector Machines”, In Proceedings of the Human Language Technology Conference of the North American Chapter of the Association for Computational Linguistics, Short Papers (NAACL/HLT-2007), pp. 81-84, Rochester, NY, April 2007.
[34] Saneem Ahmed C.G,Harikrishna Narasimhan,Shivani Agarwal,"Bayes Optimal Feature Selection for Supervised Learning with General Performance Measures”.
[35] R. Sathya, Annamma Abraham,” Comparison of Supervised and Unsupervised Learning Algorithms for Pattern Classification”, (IJARAI) International Journal of Advanced Research in Artificial Intelligence, Vol. 2, No. 2, 2013.
[36] Satinder Singh, Andrew G. Barto, Nuttapong Chentanez,” Intrinsically Motivated Reinforcement Learning”, NSF grant CCF 0432027 and by a grant from DARPA’s IPTO program.
[37] Shoushan Li, Zhongqing Wang, Guodong Zhou, Sophia Yat Mei Lee,” Semi-Supervised Learning for Imbalanced Sentiment Classification”, Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence.
[38] Ms. Sonali. B. Maind, Ms. Priyanka Wankar,” Research Paper on Basic of Artificial Neural Network”, International Journal on Recent and Innovation Trends in Computing and Communication, ISSN: 2321-8169.
[39] B. H. Sreenivasa Sarma, B. Ravindran,” Intelligent Tutoring Systems using Reinforcement Learning to teach Autistic Students”.
[40] Steve Dini ,Mark Serrano,” Combining Q-Learning with Artificial Neural Networks in an Adaptive Light Seeking Robot”, International Joint Conference on Neural Networks.
[41] Stuart Russell, Andrew L. Zimdars,”Q-Decomposition for Reinforcement Learning Agents”, Computer Science Division, University of California, Berkeley, Berkeley CA 94720-1776 USA.
[42] Taylor Berg-Kirkpatrick, Alexandre Bouchard-Cot, John DeNero,Dan Klein,” Painless Unsupervised Learning with Features”, Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the ACL.
[43] Tim Paek,” Reinforcement Learning for Spoken Dialogue Systems: Comparing Strengths and Weaknesses for Practical Deployment”, Microsoft Research One Microsoft Way, Redmond, WA 98052.
[44] Timothy P. Jurka, Loren Collingwood, Amber E. Boydstun, Emiliano Grossman, and Wouter van Atteveldt,” RTextTools: A Supervised Learning Package for Text Classification” ,The R Journal Vol.5/1 June ISSN 2073-4859.
[45] Vibha Soni, Meenakshi R Patel,” Unsupervised Opinion Mining From Text Reviews Using SentiWordNet”, International Journal of Computer Trends and Technology (IJCTT) – volume 11 number 5 – May 2014.
[46] Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Alex Graves, Ioannis Antonoglou, Daan Wierstra ,Martin Riedmiller,” Playing Atari with Deep Reinforcement Learning”, DeepMind Technologies.
[47] Xiang Wang, David Sontag, Fei Wang,”Unsupervised Learning of Disease Progression Models”, KDD’14, August 24–27, 2014, New York, NY, USA.
[48] Xiaojin Zhu, John Lafferty, Zoubin Ghahramani,” Combining Active Learning and Semi-Supervised Learning Using Gaussian Fields and Harmonic Functions”, Proceedings of the ICML-2003 Workshop on The Continuum from Labeled to Unlabeled Data, Washington DC, 2003.
[49] Xinghao Pan, Joseph Gonzalez,Stefanie Jegelka,Tamara Broderick, Michael I. Jordan, “Optimistic Concurrency Control for Distributed Unsupervised Learning”.
[50] Yong Cao, Petros Faloutsos, Frédéric Pighin,” Unsupervised Learning for Speech Motion Editing”, Eurographics/SIGGRAPH Symposium on Computer Animation (2003).
[51] Yu-Feng Li, James T. Kwok, Zhi-Hua Zhou,” Semi-Supervised Learning Using Label Mean,” Proceedings of the 26 th International Conference on Machine Learning”, Montreal, Canada, 2009. Copyright 2009 by the author(s)/owner(s).
[52] Yuriy Nevmyvaka, Yi Feng, Michael Kearns,” Reinforcement Learning for Optimized Trade Execution”, Proceedings of the 23 rd International Conference on Machine Learning, Pittsburgh, PA, 2006.
[53] M.Subba Rao, Dr.B.Eswara Reddy,” Comparative Analysis of Pattern Recognition Methods: An Overview”, Indian Journal of Computer Science and Engineering, Vol. 2 No. 3 Jun-Jul 2011.