… algorithm was used to obtain the optimal solution in the multi-objective optimization function
space… the liquid penetrated into the cracks,the two-dimensional liquid-filled model of a rolling …

… One dimensional and two dimensional flow modeling tools were deployed to calculate and
… We also lumped streets and sidewalks into this open space category. Open space received a …

… to map the two-dimensional data into a high dimensional feature space and use independent
… structure, which is exploited to derive a new angle-based spectral clustering algorithm …

… Monte Carlo simulations of the two-dimensional spin-l square lattice … alternates between
the spin and graph configuration spaces: … In cluster algorithm we need to identify clusters. It …

… such that at each point, s, in space, a range of values exists for an … a probability distribution.
The set of random variables, {Z(s): s∈D}, where D is a subset of two-dimensional space (R 2 )…

… , the K-means method represents a popular clustering algorithm used to divide a … a two-dimensional
lattice that acts as the output layer of the SOM, where high-dimensional input space …

… of nonlinearity with data lying on a two-dimensional “Swiss roll”: … in the high-dimensional
input space, as measured by their straight… that with probability at least 1- μ, estimates of the form …

… interpretations of results obtained from two-dimensional (2D) data, such as delineating
structures, anomalies, or targets, without incorporating quantitative approaches. This oversight is …

… In a two-dimensional factor space the model form can be determined as easily as it can
be visualized in a scatter plot. Subsequently, reliable parameter estimates may be obtained. …

… be thought of as a set of twodimensional, scalar-valued layers … If we assume equal a priori
probability for each source … There are several clusters in this one-dimensional space, and any …