By Roger Lee
This edited e-book offers medical result of the 16th IEEE/ACIS foreign convention on software program Engineering, synthetic Intelligence, Networking and Parallel/Distributed Computing (SNPD 2015) which used to be hung on June 1 – three, 2015 in Takamatsu, Japan. the purpose of this convention used to be to assemble researchers and scientists, businessmen and marketers, lecturers, engineers, laptop clients, and scholars to debate the varied fields of machine technological know-how and to proportion their reports and alternate new principles and knowledge in a significant means. study effects approximately all elements (theory, functions and instruments) of machine and knowledge technological know-how, and to debate the sensible demanding situations encountered alongside the best way and the ideas followed to unravel them.
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Extra resources for Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing 2015
A total dose distribution is obtained by a superposition of the contributions from elementary 24 G. Kalantzis et al. Fig. 5 Lateral distributions as a function of distance from the central axis for various depths d in cm Fig. 6 Schematic representation of dose deposition into a particular voxel (x, y, z) from the PBi . The lateral Gaussian-shaped curves represent the lateral penumbra of the PBi pencil beams. The total dose D(x, y, z) at point (x, y, z) is calculated as follows: D(x, y, z) = dx dy C (x , y ) (z)O(x − x , y − y , z) (17) C(x , y ) (z) indicates the central-axis term of the beam passing through a point (x , y , z).
In order to determine this depth-dose curve, we only need to know the functions E(z) and (z). 5, this relationship is known as Geiger’s rule, which is valid for protons with energies up to about 10 MeV. 8. The factor α is approximately proportional to the square root of the effective atomic mass of the absorbing medium. It should be noted that this simplified range-energy relationship is valid in good approximation for arbitrary media and various particles with atomic masses approximately between one (protons) and twelve (carbon ions) .
6 Conclusions In this paper, we proposed an incremental model for semi-supervised learning, the model use two notable properties: 1. convex of objective function. we can design efficient online algorithm with logarithmic regret, it achieve accurate solution with very little overhead. moreover, while the data is processed one after one, it can also response to prediction request in training stage; 2. max-margin learning, which provide our model with considerable generalization capability for future unseen data.