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weights

Shortest Paths with Multiplicative Cost. In a given undirected graph, the path cost is measured as a

Shortest Paths with Multiplicative Cost. In a given undirected graph, the path cost is measured as a product of all the edges in the path. The weights are rational numbers (e.g., 0.25, 0.75, 3.75 etc) or integers (2, 3). There are no negative edges. Given such a graph as input, you are to output the shortest path between any two given vertices. Input is the adjacency matrix and the two vertices. You must output the path.

標簽： Multiplicative undirected Shortest measured

上傳時間： 2017-04-08

上傳用戶：邶刖
Implementation of Edmonds Karp algorithm that calculates maxFlow of graph. Input: For each test c

Implementation of Edmonds Karp algorithm that calculates maxFlow of graph. Input: For each test case, the first line contains the number of vertices (n) and the number of arcs (m). Then, there exist m lines, one for each arc (source vertex, ending vertex and arc weight, separated by a space). The nodes are numbered from 1 to n. The node 1 and node n should be in different sets. There are no more than 30 arcs and 15 nodes. The arc weights vary between 1 and 1 000 000. Output: The output is a single line for each case, with the corresponding minimum size cut. Example: Input: 7 11 1 2 3 1 4 3 2 3 4 3 1 3 3 4 1 3 5 2 4 6 6 4 5 2 5 2 1 5 7 1 6 7 9 Output: 5

標簽： Implementation calculates algorithm Edmonds

上傳時間： 2014-01-04

上傳用戶：kiklkook
LatentSVM論文

The object detector described below has been initially proposed by P.F. Felzenszwalb in [Felzenszwalb2010]. It is based on a Dalal-Triggs detector that uses a single filter on histogram of oriented gradients (HOG) features to represent an object category. This detector uses a sliding window approach, where a filter is applied at all positions and scales of an image. The first innovation is enriching the Dalal-Triggs model using a star-structured part-based model defined by a “root” filter (analogous to the Dalal-Triggs filter) plus a set of parts filters and associated deformation models. The score of one of star models at a particular position and scale within an image is the score of the root filter at the given location plus the sum over parts of the maximum, over placements of that part, of the part filter score on its location minus a deformation cost easuring the deviation of the part from its ideal location relative to the root. Both root and part filter scores are defined by the dot product between a filter (a set of weights) and a subwindow of a feature pyramid computed from the input image. Another improvement is a representation of the class of models by a mixture of star models. The score of a mixture model at a particular position and scale is the maximum over components, of the score of that component model at the given location.

標簽： 計算機視覺

上傳時間： 2015-03-15

上傳用戶：sb_zhang
Bi-density twin support vector machines

In this paper we present a classifier called bi-density twin support vector machines (BDTWSVMs) for data classification. In the training stage, BDTWSVMs first compute the relative density degrees for all training points using the intra-class graph whose weights are determined by a local scaling heuristic strategy, then optimize a pair of nonparallel hyperplanes through two smaller sized support vector machine (SVM)-typed problems. In the prediction stage, BDTWSVMs assign to the class label depending on the kernel density degree-based distances from each test point to the two hyperplanes. BDTWSVMs not only inherit good properties from twin support vector machines (TWSVMs) but also give good description for data points. The experimental results on toy as well as publicly available datasets indicate that BDTWSVMs compare favorably with classical SVMs and TWSVMs in terms of generalization

標簽： recognition Bi-density machines support pattern vector twin for

上傳時間： 2019-06-09

上傳用戶：lyaiqing