177,698 research outputs found
Efficient classification using parallel and scalable compressed model and Its application on intrusion detection
In order to achieve high efficiency of classification in intrusion detection,
a compressed model is proposed in this paper which combines horizontal
compression with vertical compression. OneR is utilized as horizontal
com-pression for attribute reduction, and affinity propagation is employed as
vertical compression to select small representative exemplars from large
training data. As to be able to computationally compress the larger volume of
training data with scalability, MapReduce based parallelization approach is
then implemented and evaluated for each step of the model compression process
abovementioned, on which common but efficient classification methods can be
directly used. Experimental application study on two publicly available
datasets of intrusion detection, KDD99 and CMDC2012, demonstrates that the
classification using the compressed model proposed can effectively speed up the
detection procedure at up to 184 times, most importantly at the cost of a
minimal accuracy difference with less than 1% on average
Computation of protein geometry and its applications: Packing and function prediction
This chapter discusses geometric models of biomolecules and geometric
constructs, including the union of ball model, the weigthed Voronoi diagram,
the weighted Delaunay triangulation, and the alpha shapes. These geometric
constructs enable fast and analytical computaton of shapes of biomoleculres
(including features such as voids and pockets) and metric properties (such as
area and volume). The algorithms of Delaunay triangulation, computation of
voids and pockets, as well volume/area computation are also described. In
addition, applications in packing analysis of protein structures and protein
function prediction are also discussed.Comment: 32 pages, 9 figure
How to Integrate a Polynomial over a Simplex
This paper settles the computational complexity of the problem of integrating
a polynomial function f over a rational simplex. We prove that the problem is
NP-hard for arbitrary polynomials via a generalization of a theorem of Motzkin
and Straus. On the other hand, if the polynomial depends only on a fixed number
of variables, while its degree and the dimension of the simplex are allowed to
vary, we prove that integration can be done in polynomial time. As a
consequence, for polynomials of fixed total degree, there is a polynomial time
algorithm as well. We conclude the article with extensions to other polytopes,
discussion of other available methods and experimental results.Comment: Tables added with new experimental results. References adde
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