Download Advances in Swarm Intelligence: Third International by Seyed Naser Razavi, Nicolas Gaud, Abderrafiâa Koukam, Naser PDF

By Seyed Naser Razavi, Nicolas Gaud, Abderrafiâa Koukam, Naser Mozayani (auth.), Ying Tan, Yuhui Shi, Zhen Ji (eds.)

This publication and its better half quantity, LNCS vols. 7331 and 7332, represent the complaints of the 3rd overseas convention on Swarm Intelligence, ICSI 2012, held in Shenzhen, China in June 2012. The one hundred forty five complete papers provided have been conscientiously reviewed and chosen from 247 submissions. The papers are equipped in 27 cohesive sections overlaying all significant issues of swarm intelligence examine and developments.

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1 Introduction For e-Learning system, the main benefit of Ajax is a greatly improved user experience. Although JavaScript and DHTML—the technical foundations of Ajax— have been available for years, most programmers ignored them because they were difficult to master. Although most of the Ajax frameworks available today simplify development work, you still need a good grasp of the technology stack. So, if you’re planning to use Ajax to improve only your application’s user experience—if you’re not also using it as a strategic advantage for your business—it may be unwise to spend a lot of money and time on the technology.

Morgan Kaufmann Publishers, New York (2002) 16. : Introduction to evolutionary computing, 1st edn. Natural Computing Series. com Abstract. In this paper, we investigate to use the L1/2 regularization method for variable selection based on the Cox’s proportional hazards model. The L1/2 regularization method is a reweighed iterative algorithm with the adaptively weighted L1 penalty on regression coefficients. The algorithm of the L1/2 regularization method can be easily obtained by a series of L1 penalties.

Finally we give a brief discussion. 2 L1/2 Regularization The L1/2 regularization for linear regression can be expressed as: βˆ = arg min{ ( 1 n ∑ Yi − X iT β n i =1 ) 2 p + λ ∑ βi } (6) i =1 The experiments show that the solutions yielded from the L1/2 penalty are more sparse and can predicate better than those from the L1 penalty. On the other hand, solving the L1/2 penalty is much simpler than solving the L0 penalty. All these properties support the usefulness of the L1/2 penalty and the L1/2 penalty can be potentially more powerful than the L0 and L1 penalties in real applications.

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