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The concept of organizational learning
Reflection on organizational learning
The concept of organizational learning
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Learning ABSTRACT The concept of learning may be regarded as any process through which a system utilizes knowledge to improve its performance. As we move into the age of digital information, the rapid and explosive growth of external, as well as, internal data and information that organizations are faced with is a problem that they are currently trying to overcome. The ability to collect and store this data is far ahead of the ability to analyze and learn from it. The concept of learning will be examined from the perspective of the inferential learning theory. This theory examines the mix of input knowledge, background knowledge, learning objectives or goals and an inference process to obtain 'new' or 'learned' knowledge. Various learning situations may dictate differing learning processes. The three that will be briefly highlighted in this paper are; learning by induction, through the use of decision rules or decision trees; learning by discovery; and learning by taking advice, explanation-based generalization. The concept of multi-strategy learning in order to handle more complex problems will also be examined. INTRODUCTION Research in the area of learning has been ongoing for several years, and it has over the years been traditionally characterized as an improvement in a system's behavior or knowledge due to its experience. "Experience" in this context is looking at the totality of information generated in the course of performing some action. The inferential theory of learning suggests a means of our understanding the learning process. Michalski 1 proposes that this theory assumes that learning is a goal-guided process of modifying the learner's knowledge by exploring the learner's experience. This process he... ... middle of paper ... ...fman R. A. - "Data Mining and Knowledge Discovery" - A Review of issues and Multi- strategy Approach". Reports of the Machine Learning and Inference Laboratory, MCI 97-2, George Mason University, Fairfax, V.A. 1997. http://www.mli.gmu.edu/~kaufman/97-1.ps 6 Chun-Nan Hsu and Craig A. Knoblock - "Discovering Robust Knowledge from Dynamic Closed - World Data". http://www.isi.edu/sims/papers/95-robust.ps 7 Shavlik Jude W. - "Acquiring Recursive and Iterative Concepts with Explanation-Based Learning". Machine Learning Vol. 5,(1990). 8 Tecuci Gheorghe - "Plausible Justification Trees: A framework for Deep and Dynamic Integration of Learning Strategies", Machine Learning Vol. 11(1993). 9 Fayyad U., Piatetsky-Shapiro G., Smyth, Padhraic - "The KDD Process for Extracting Useful Knowledge from volumes of Data" - Communications of the ACM vol. 39, no. 11 (Nov. 1996).