By Chu Kiong Loo, Yap Keem Siah, Kok Wai Wong, Andrew Teoh Beng Jin, Kaizhu Huang
The 3 quantity set LNCS 8834, LNCS 8835, and LNCS 8836 constitutes the lawsuits of the 20 th overseas convention on Neural details Processing, ICONIP 2014, held in Kuching, Malaysia, in November 2014. The 231 complete papers offered have been rigorously reviewed and chosen from 375 submissions. the chosen papers hide significant themes of theoretical learn, empirical examine, and functions of neural info processing study. the three volumes symbolize topical sections containing articles on cognitive technology, neural networks and studying platforms, conception and layout, functions, kernel and statistical tools, evolutionary computation and hybrid clever structures, sign and snapshot processing, and designated classes clever structures for helping determination, making techniques, theories and purposes, cognitive robotics, and studying platforms for social community and net mining.
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Extra resources for Neural Information Processing: 21st International Conference, ICONIP 2014, Kuching, Malaysia, November 3-6, 2014. Proceedings, Part III
ICONIP 2014, Part III, LNCS 8836, pp. 25–32, 2014. © Springer International Publishing Switzerland 2014 26 C. Yang and X. Gu texture and wavelet moments. Meanwhile, more effective similarity measurements and metric learning have also been developed for image retrieval. For instance, histogram intersection  is more effective for histogram representation than traditional L2 distance. These simple methods have two disadvantages. The first is that they usually cannot obtain satisfying retrieval results on large image database because their corresponding similarity measurements donot conform to the human perception well.
15, 1069–1072 (2003) 5. : Adaptive color feature extraction based on image color distributions. IEEE Trans. Image Processing 19, 2005–2016 (2010) 6. : Color image processing by using binary quaternion moment-preserving thresholding technique. IEEE Trans. Image Processing 8, 614–628 (1999) 7. : PCNN models and applications. IEEE Trans. on Neural Networks 10, 480–498 (1999) 8. : Feature extraction using unit-linking pulse coupled neural network and its applications. Neural Process. Lett. 27, 25–41 (2007) 9.
We evaluate its performance over a real-world advertising dataset and the results demonstrate that the improved cube factorization model outperforms the matrix factorization. Keywords: click through rate estimation, demand-side platform, real time bidding, cube factorization model. 1 Introduction In the real time bidding (RTB) display advertising ecosystem, ad exchanges aggregate ad impressions from multiple publishers and send them to several demand-side platforms (DSPs) via real time auction.