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Spin‐Split Edge States in Metal‐Supported Graphene Nanoislands Obtained by CVD
Advanced Materials, EarlyView.Combining STM measurements and ab‐initio calculations, we show that zig‐zag edges in graphene nanoislands grown on Ni(111) by CVD retrieve their spin‐polarized edge states after intercalation of a few monolayers of Au. ABSTRACT Spin‐split states localized on zigzag edges have been predicted for different free‐standing graphene nanostructures.
Michele Gastaldo +6 more
wiley +1 more source
Weighted K-Nearest Neighbor revisited
2016 23rd International Conference on Pattern Recognition (ICPR), 2016 In this paper we show that weighted K-Nearest Neighbor, a variation of the classic K-Nearest Neighbor, can be reinterpreted from a classifier combining perspective, specifically as a fixed combiner rule, the sum rule. Subsequently, we experimentally demonstrate that it can be rather beneficial to consider other combining schemes as well. In particular,
BICEGO, Manuele, Loog, M.
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K-Nearest Neighbor Finding Using MaxNearestDist
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2008Similarity searching often reduces to finding the k nearest neighbors to a query object. Finding the k nearest neighbors is achieved by applying either a depth- first or a best-first algorithm to the search hierarchy containing the data. These algorithms are generally applicable to any index based on hierarchical clustering.
Hanan Samet
exaly +3 more sources
Weighting of the k-Nearest-Neighbors
2010 20th International Conference on Pattern Recognition, 2010This paper presents two distribution independent weighting schemes for k-Nearest-Neighbors (kNN). Applying the first scheme in a Leave-One-Out (LOO) setting corresponds to performing complete b-fold cross validation (b-CCV), while applying the second scheme corresponds to performing bootstrapping in the limit of infinite iterations. We demonstrate that
Chernoff, Konstantin, Nielsen, Mads
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2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019
Hashing based approximate nearest neighbor search embeds high dimensional data to compact binary codes, which enables efficient similarity search and storage. However, the non-isometry sign() function makes it hard to project the nearest neighbors in continuous data space into the closest codewords in discrete Hamming space.
Xiangyu He +2 more
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Hashing based approximate nearest neighbor search embeds high dimensional data to compact binary codes, which enables efficient similarity search and storage. However, the non-isometry sign() function makes it hard to project the nearest neighbors in continuous data space into the closest codewords in discrete Hamming space.
Xiangyu He +2 more
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Improved k-nearest neighbor classification
Pattern Recognition, 2002zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Yingquan Wu +2 more
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A fuzzy K-nearest neighbor algorithm
IEEE Transactions on Systems, Man, and Cybernetics, 1985Classification of objects is an important area of research and application in a variety of fields. In the presence of full knowledge of the underlying probabilities, Bayes decision theory gives optimal error rates. In those cases where this information is not present, many algorithms make use of distance or similarity among samples as a means of ...
James M. Keller +2 more
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An adaptive k-nearest neighbor algorithm
2010 Seventh International Conference on Fuzzy Systems and Knowledge Discovery, 2010An adaptive k-nearest neighbor algorithm (AdaNN) is brought forward in this paper to overcome the limitation of the traditional k-nearest neighbor algorithm (kNN) which usually identifies the same number of nearest neighbors for each test example. It is known that the value of k has crucial influence on the performance of the kNN algorithm, and our ...
Shiliang Sun, Rongqing Huang
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2020
Please download the sample Excel files from https://github.com/hhohho/Learn-Data-Mining-through-Excel for this chapter’s exercises.
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Please download the sample Excel files from https://github.com/hhohho/Learn-Data-Mining-through-Excel for this chapter’s exercises.
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