Intelligent Data Engineering and Automated Learning – IDEAL by Hujun Yin, Yang Gao, Bin Li, Daoqiang Zhang, Ming Yang, Yun

By Hujun Yin, Yang Gao, Bin Li, Daoqiang Zhang, Ming Yang, Yun Li, Frank Klawonn, Antonio J. Tallón-Ballesteros

The middle of the correct convention has been evolving from mining, examining, and exploring to studying and making feel of seas of information this can be really significant during this big-data and deep-learning period.

The sixty eight complete papers provided have been rigorously reviewed and chosen from a hundred and fifteen submissions. they supply a worthwhile and well timed pattern of contemporary study results in facts engineering and automatic studying starting from methodologies, frameworks, and strategies to functions together with quite a few issues corresponding to evolutionary algorithms deep studying neural networks probabilistic modeling particle swarm intelligence substantial facts research purposes in regression, category, clustering, scientific and organic modeling and predication textual content processing and photo research.

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Extra info for Intelligent Data Engineering and Automated Learning – IDEAL 2016: 17th International Conference, Yangzhou, China, October 12–14, 2016, Proceedings

Example text

The magic square of order n is a matrix of n × n, which is filled with unrepeated numbers 1,2,…,n2, and the sum of the elements in each row, column, diagonal is a definite number called magic number with n(n2 + 1)/2. An unfinished magic square may look as in Fig. 1. The initial set of occupied cells is called the “clues” of the magic square. The objective is to fill the empty cells. Magic square was first used in astrology, prophecy and interpretation of philosophy, natural phenomena and human behavior.

Beijing Science Press, Beijing (2008) 3. : Numerous but rare: an exploration of magic squares. PLoS ONE 10, 49–55 (2015) 4. : A rapid condition combinatorial algorithm. J. Zhejiang Norm. Univ. ) 29, 52–55 (2006) 36 Y. Liang et al. 5. : Research On Intelligent Computation Of Magic Square Problem. National University of Defense Technology Engineering Master Thesis (2005) 6. : Magic square spectra. Linear Algebra Appl. 430, 2659–2680 (2009) 7. : On properties of special magic square matrices. Linear Algebra Appl.

P/q) − 1) (p/q) − 1) Thus this proposition is also true for d = k. We can see that R can help state space search in finding an exit of Gd if (p/q)d+1 − 1 tptl ≥ . (p/q) − 1 E We can derive a necessary condition for the above inequality by replacing the left side as (p/q)d+1 . In this case, any tl must satisfy d ≤ tl ≤ d + logp E − (d + 1) (1 − logp q) − logp t. It is easy to see that as q increases from 1 to p, random walk exploration becomes less effective. The insight is that RW-BFS is more effective when there are not many paths (q is smaller compared to p) that can lead to the same state.

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