Output-Sensitive Algorithms for Computing Nearest-Neighbour Decision Boundaries
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Cites methods from "Output-Sensitive Algorithms for Com..."
...In this subsection, simulations using V-ELM, SVM [12], OP-ELM [28], BP [9,24,27], and KNN [7,2] are conducted on all the above 19 datasets....
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...For KNN, 7 nearest neighbors are used and the Euclidean norm is adopted to calculate the distance....
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...Simulations on many real world classification datasets demonstrate that V-ELM outperforms several recent methods in general, including the original ELM [14], support vector machine (SVM) [12], optimally pruned extreme learning machine (OP-ELM) [28], Back-Propagation algorithm (BP) [9,24,27], K nearest neighbors algorithm (KNN) [2,7], robust fuzzy relational classifier (RFRC) [5], radial basis function neural network (RBFNN) [33] and multiobjective simultaneous learning framework (MSCC) [6]....
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...In this section, the performance of the proposed V-ELM is compared with the original ELM [14], SVM [12] and several other recent classification methods, including OP-ELM [28], Back-Propagation algorithm (BP) [9,24,27], K nearest neighbors algorithm (KNN) [2,7], RFRC [5], RBFNN [33] and MSCC [6]....
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...It is easy to find from this table that with K = 7, V-ELM learns much faster than SVM and BP for all the datasets, faster than OP-ELM in all the datasets except for Iris, Monk1 and Monk2, and faster than KNN in all the datasets except for Soybean, Hayes and Protein....
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178 citations
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Cites methods from "Output-Sensitive Algorithms for Com..."
...To generate a mean and a variance for the flower visitation rate within each cell of the temperature-time grid, we used the k-nearest neighbour algorithm (Bremner et al., 2005)....
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...To generate a mean and a variance for the flower visitation rate within each cell of the temperature-time grid, we used the k-nearest neighbour algorithm (Bremner et al., 2005)....
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References
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"Output-Sensitive Algorithms for Com..." refers background in this paper
...Several properties make the nearest-neighbour decision rule quite attractive, including its intuitive simplicity and the theorem that the asymptotic error rate of the nearestneighbour rule is bounded from above by twice the Bayes error rate [6], [8], [16]....
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1,384 citations
"Output-Sensitive Algorithms for Com..." refers methods in this paper
...To find the decision boundary in O(n log k) time, we begin by computing the median element m = s� n/2 in O(n) time using any one of the existing linear-time median finding algorithms (see [ 3 ])....
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"Output-Sensitive Algorithms for Com..." refers methods in this paper
...Alternatively, the algorithm we describe for computing the nearest-neighbour decision boundary actually produces the Vorono˘ i diagram of the condensed set (which has size O(k)) that can be preprocessed in O(k) time by Kirkpatrick’s point-location algorithm [ 12 ] to allow nearest-neighbour classification in O(log k) time....
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