A Survey on Machine Learning: Concept,Algorithms and Applications

Over the past few decades, Machine Learning (ML) has evolved from the endeavour of few computer enthusiasts exploiting the possibility of computers learning to play games, and a part of Mathematics (Statistics) that seldom considered computational approaches, to an independent research discipline that has not only provided the necessary base for statistical-computational principles of learning procedures, but also has developedvarious algorithms that are regularly used for text interpretation, pattern recognition, and a many other commercial purposes and has led to a separate research interest in data mining to identify hidden regularities or irregularities in social data that growing by second. This paper focuses on explaining the concept and evolution of Machine Learning, some of the popular Machine Learning algorithms and try to compare three most popular algorithms based on some basic notions. Sentiment140 dataset was used and performance of each algorithm in terms of training time, prediction time and accuracy of prediction have been documented and compared.

[1]  Shih-Fu Chang,et al.  Semi-supervised learning using greedy max-cut , 2013, J. Mach. Learn. Res..

[2]  Ben Taskar,et al.  Learning from Partial Labels , 2011, J. Mach. Learn. Res..

[3]  Xinlei Chen,et al.  NEIL: Extracting Visual Knowledge from Web Data , 2013, 2013 IEEE International Conference on Computer Vision.

[4]  Jaime G. Carbonell,et al.  Proactive learning: cost-sensitive active learning with multiple imperfect oracles , 2008, CIKM '08.

[5]  T. Ben-David,et al.  Exploiting Task Relatedness for Multiple , 2003 .

[6]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

[7]  Jonathan Baxter,et al.  A Model of Inductive Bias Learning , 2000, J. Artif. Intell. Res..

[8]  Nello Cristianini,et al.  An introduction to Support Vector Machines , 2000 .

[9]  Mohamed Cheriet,et al.  Genetic algorithm–based training for semi-supervised SVM , 2010, Neural Computing and Applications.

[10]  Rolf Oppliger,et al.  Internet security: firewalls and beyond , 1997, CACM.

[11]  Taiwo Oladipupo Ayodele,et al.  Types of Machine Learning Algorithms , 2010 .

[12]  Bhavani M. Thuraisingham,et al.  Design of LDV: a multilevel secure relational database management system , 1990 .

[13]  Thomas G. Dietterich,et al.  Transfer Learning with an Ensemble of Background Tasks , 2005, NIPS 2005.

[14]  Estevam R. Hruschka,et al.  Toward an Architecture for Never-Ending Language Learning , 2010, AAAI.

[15]  H. Hlynsson,et al.  Transfer Learning Using the Minimum Description Length Principle with a Decision Tree Application , 2007 .

[16]  Federico Girosi,et al.  Support Vector Machines: Training and Applications , 1997 .

[17]  Ramakrishnan Srikant,et al.  Privacy-preserving data mining , 2000, SIGMOD '00.

[18]  Richard Conway,et al.  Selective partial access to a database , 1976, ACM '76.

[19]  S. Sathiya Keerthi,et al.  Optimization Techniques for Semi-Supervised Support Vector Machines , 2008, J. Mach. Learn. Res..

[20]  Qiang Yang,et al.  Transferring Naive Bayes Classifiers for Text Classification , 2007, AAAI.