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  Browse All Reviews > Computing Methodologies (I) > Artificial Intelligence (I.2) > Learning (I.2.6) > Induction (I.2.6...)  
  1-10 of 55 Reviews about "Induction (I.2.6...)": Date Reviewed
  The impact of semi-supervised clustering on text classification
Kyriakopoulou A., Kalamboukis T.  PCI 2013 (Proceedings of the 17th Panhellenic Conference on Informatics, Thessaloniki, Greece,  Sep 19-21, 2013) 180-187, 2013. Type: Proceedings

Categorizing texts into predefined classes is an important issue relevant to many fields, including data mining, natural language processing, and machine learning. In this paper, the authors build on previous work that investigates how building ne...

Apr 21 2014
  The inductive software engineering manifesto: principles for industrial data mining
Menzies T., Bird C., Zimmermann T., Schulte W., Kocaganeli E.  MALETS 2011 (Proceedings of the International Workshop on Machine Learning Technologies in Software Engineering, Lawrence, KS,  Nov 12, 2011) 19-26, 2011. Type: Proceedings

Lessons learned from data mining projects in industry are presented in the form of seven principles and a dozen tips. No questionnaire or interview surveys were administered. The lessons learned are simply drawn from the authors’ own extensi...

Dec 18 2012
  Multiple-view multiple-learner semi-supervised learning
Sun S., Zhang Q.  Neural Processing Letters 34(3): 229-240, 2011. Type: Article

In semi-supervised learning, a small amount of labeled data is combined with a large set of unlabeled data, which can lead to significant improvements in learning accuracy. One technique to achieve this is co-training, in which multiple learners a...

Feb 9 2012
   Probabilistic graphical models: principles and techniques
Koller D., Friedman N.,  The MIT Press, Cambridge, MA, 2009. 1208 pp. Type: Book (978-0-262013-19-2)

Efforts over the past 60 years to use computers to implement human-like reasoning have favored the interpretation of probabilities as reflecting degrees of belief, fueling the rapid growth of Bayesian formalisms. While theoretically attractive, th...

Oct 6 2010
  NEWPAR: an automatic feature selection and weighting schema for category ranking
Ruiz-Rico F., Vicedo J., Rubio-Sánchez M.  Document engineering (Proceedings of the 2006 ACM Symposium on Document Engineering, Amsterdam, The Netherlands,  Oct 10-13, 2006) 128-137, 2006. Type: Proceedings

The classification of plain-text documents is an ongoing challenge in information research. This paper proposes an original mixture of existing ideas for the categorization of plain-text documents....

Jan 12 2007
  Genetic wrappers for feature selection in decision tree induction and variable ordering in Bayesian network structure learning
Hsu W.  Information Sciences 163(1-3): 103-122, 2004. Type: Article

A Bayesian network is a graphical model that allows the encoding of probabilistic relationships among different measurable features of input data, and proves to be a suitable framework for data modeling when used in conjunction with statistical te...

Jul 7 2005
  Top-Down Induction of Model Trees with Regression and Splitting Nodes
Malerba D., Esposito F., Ceci M., Appice A.  IEEE Transactions on Pattern Analysis and Machine Intelligence 26(5): 612-625, 2004. Type: Article

Decision trees partition the input space into hyper-rectangles. Regression trees are decision trees that fit a regression model to the data in the hyper-rectangle described by the leaf of a tree. Model trees are a mix of regression trees and class...

Apr 6 2005
  Leave one out error, stability, and generalization of voting combinations of classifiers
Evgeniou T., Pontil M., Elisseeff A.  Machine Learning 55(1): 71-97, 2004. Type: Article

The assessment and explanation of why, when, and how much the use of a combination of classifiers is better than the use of a single classifier is one of the most persistent topics in machine learning. In this paper, the authors study the generali...

Mar 11 2005
  Exploiting response models: optimizing cross-sell and up-sell opportunities in banking
Cohen M.  Information Systems 29(4): 327-341, 2004. Type: Article

Optimizing the ability to sell new features to existing customers, and determining what features to sell to prospects, is a difficult challenge for the banking industry. This paper proposes solutions to those problems....

Feb 3 2005
  Feature selection with conditional mutual information maximin in text categorization
Wang G., Lochovsky F.  Information and knowledge management (Proceedings of the Thirteenth ACM Conference on Information and Knowledge Management, Washington, D.C, USA,  Nov 8-13, 2004) 342-349, 2004. Type: Proceedings

The main, and very often computationally overwhelming, characteristic of text data is its extremely high dimensionality, which could prove to be a severe obstacle for any classification algorithm. One of the most frequently used ways to reduce dim...

Jan 26 2005
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