By Peng Wu, Hui Zhao (auth.), Ran Chen (eds.)
This two-volume set (CCIS 134 and CCIS one hundred thirty five) constitutes the refereed court cases of the foreign convention on clever Computing and data technological know-how, ICICIS2011, held in Chongqing, China, in January 2011. The 226 revised complete papers awarded in either volumes, CCIS 134 and CCIS one hundred thirty five, have been conscientiously reviewed and chosen from over six hundred preliminary submissions. The papers give you the reader with a vast evaluate of the most recent advances within the box of clever computing and knowledge science.
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Additional info for Intelligent Computing and Information Science: International Conference, ICICIS 2011, Chongqing, China, January 8-9, 2011. Proceedings, Part I
Sample text
In this way, incorrectly classified samples will be protruded, thus 4 P. Wu and H. Zhao obtaining a new distribution of samples. Under the new distribution of samples, train the weak classifiers once again and get weak classifiers. Then according to this, after the loops of T times, T weak classifiers can be obtained. Boost these T weak classifiers by a certain weight and then get the final desired strong classifier. Do some explanation for the output decision-making expression of the finally generated strong classifier: 1 ⎡ T ⎤ ⎛ 1− εt ⎞ ⎛ 1− εt ⎞2 ⎥ 1− εt 1 1 ⎢ = ∑ log ⎜ log ⎟ = log ⎢∏ ⎜ ⎟ ⎥ ∑ αt = ∑ 2 t =1 εt t =1 2 t =1 t =1 ⎝ ε t ⎝ εt ⎠ ⎠ ⎥ ⎢⎣ ⎦ T T 1 2 T ht ( xi ) ⎛ 1− εt ⎞ 1 ht ( xi ) − ⎤ ⎡ T ⎜ ⎟ ∏ T T 2 ε ⎛ ⎞ − 1 ε 1 t =1 ⎝ t ⎠ ⎢ ⎥ t − = = h x α ( ) α log log ⎜ ⎟ ∑ ∑ ∏ t t i t 1 ⎢ ⎥ 2 ε t =1 t =1 t =1 ⎝ T ⎠ t ⎛ 1− εt ⎞2 ⎣⎢ ⎦⎥ ⎜ ⎟ ∏ t =1 ⎝ ε t ⎠ 1− εt > 0 , namely, the output of the final strong classifier Among them, log T εt depends on whether the decision outcome of all the weak classifiers' integrated votes is greater that the average outcome of their votes.
However, they did not discuss on the used dataset and how divided it for training and validation process. In another research, Khoshgoftaar et al. considering a case study on the real time estimation software. They used a primary neural network model to establish a real time cost estimation model [12, 13]. But the presented validation process of their model is not adequate. Shepperd and Schofield presented a software estimation model based on analogy. This research was one the basic work on that time [14].
Ow model results show better effort estimation results than other compared models. Srinivasan et al. applying machine leaning approaches based on neural networks and regression trees to algorithmic cost estimation models [11]. They reported that the main advantages of learning systems such as adaptable and nonparametric. However, they did not discuss on the used dataset and how divided it for training and validation process. In another research, Khoshgoftaar et al. considering a case study on the real time estimation software.
