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  Browse All Reviews > Computing Methodologies (I) > Artificial Intelligence (I.2) > Learning (I.2.6) > Connectionism And Neural Nets (I.2.6...)  
 
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  1-10 of 80 Reviews about "Connectionism And Neural Nets (I.2.6...)": Date Reviewed
  Opinion question answering by sentiment clip localization
Pang L., Ngo C. ACM Transactions on Multimedia Computing, Communications, and Applications 12(2): 1-19, 2015.  Type: Article

This application of artificial intelligence (AI) demonstrates two things: the astonishing types of questions algorithms can answer today, and (implicitly) what challenges and limitations we currently still face despite the current rene...

Apr 12 2016
  Using artificial neural networks to predict first-year traditional students second year retention rates
Plagge M.  ACMSE 2013 (Proceedings of the 51st ACM Southeast Conference, Savannah, GA, Apr 4-6, 2013) 1-5, 2013.  Type: Proceedings

This paper reports the results of a project using neural networks and data supplied by the IT department of Columbus State University in Georgia to predict which students will drop out after their first year of college. The author bega...

Aug 8 2013
  An incremental online semi-supervised active learning algorithm based on self-organizing incremental neural network
Shen F., Yu H., Sakurai K., Hasegawa O. Neural Computing and Applications 20(7): 1061-1074, 2011.  Type: Article

Shen et al. propose an enhancement of a learning algorithm they developed previously. The base algorithm may be seen as a clever combination of self-organizing maps and neural gas. Since this algorithm is fairly new, literature sources...

May 17 2012
  An improved training algorithm for feedforward neural network learning based on terminal attractors
Yu X., Wang B., Batbayar B., Wang L., Man Z. Journal of Global Optimization 51(2): 271-284, 2011.  Type: Article

Yu et al. propose a new training algorithm for feed-forward neural networks, and they show that this algorithm is faster and better able to avoid being trapped in local minima, like conventional back-propagation. They also show an appl...

May 10 2012
  Social connectionism: a reader and handbook for simulations
Van Overwalle F., Routledge, New York, NY, 2007. 536 pp.  Type: Book (9781841696652)

Social psychology is the study of how people’s cognitive states or behaviors are influenced by the presence of others. Its methods are predominantly experimental, but as with any experimental science, the meaning of empirical...

May 9 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 multipl...

Feb 9 2012
  Computational intelligence for missing data imputation, estimation, and management: knowledge optimization techniques
Marwala T., Information Science Reference - Imprint of: IGI Publishing, Hershey, PA, 2009. 326 pp.  Type: Book (9781605663364)

Paradoxically, in these days of information glut, there is a concurrent problem of data loss--missing and incomplete data. Statisticians have generated a wealth of knowledge on the methods of handling missing data. While solvi...

Jan 26 2010
  Predictive mesoscale network model of cell fate decisions during C. elegans embryogenesis
Winkler D., Burden F., Halley J. Artificial Life 15(4): 411-421, 2009.  Type: Article

Winkler, Burden, and Halley’s recursive neural network--an extension of Geard and Wiles’ work [1]--models the differentiation pathway in the C. elegans worm. The neural network models stem c...

Jan 15 2010
  Associative learning on a continuum in evolved dynamical neural networks
Izquierdo E., Harvey I., Beer R. Adaptive Behavior 16(6): 361-384, 2008.  Type: Article

The primary goal of this well-written paper is to demonstrate that it is possible to model learning over a continuous range of inputs, using a continuous-time recurrent neural network (CTRNN) with fixed weights. “Learning&...

May 15 2009
  Implementation of a neural-based navigation approach on indoor and outdoor mobile robots
Azouaoui O., Kadri M., Ouadah N.  CSTST 2008 (Proceedings of the 5th International Conference on Soft Computing as Transdisciplinary Science and Technology, Cergy-Pontoise, France, Oct 28-31, 2008) 71-77, 2008.  Type: Proceedings

This paper addresses the problem of creating general navigation systems that would apply to different types of autonomous vehicles, including indoor and outdoor robots, with different physical dimensions and sensor characteristics. The...

Jan 8 2009
 
 
 
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