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227,367
Discriminant Adaptive Nearest Neighbor Classification
, 1994
"... Nearest neighbor classification expects the class conditional probabilities to be locally constant, and suffers from bias in high dimensions. We propose a locally adaptive form of nearest neighbor classification to try to ameliorate this curse of dimensionality. We use a local linear discriminant an ..."
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Cited by 322 (1 self)
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Nearest neighbor classification expects the class conditional probabilities to be locally constant, and suffers from bias in high dimensions. We propose a locally adaptive form of nearest neighbor classification to try to ameliorate this curse of dimensionality. We use a local linear discriminant
Distance metric learning for large margin nearest neighbor classification
 In NIPS
, 2006
"... We show how to learn a Mahanalobis distance metric for knearest neighbor (kNN) classification by semidefinite programming. The metric is trained with the goal that the knearest neighbors always belong to the same class while examples from different classes are separated by a large margin. On seven ..."
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Cited by 685 (15 self)
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We show how to learn a Mahanalobis distance metric for knearest neighbor (kNN) classification by semidefinite programming. The metric is trained with the goal that the knearest neighbors always belong to the same class while examples from different classes are separated by a large margin
Adaptive Metric Nearest Neighbor Classification
 IEEE Transactions on Pattern Analysis and Machine Intelligence
, 2000
"... Nearest neighbor classification assumes locally constant class conditional probabilities. This assumption becomes invalid in high dimensions with finite samples due to the curse of dimensionality. Severe bias can be introduced under these conditions when using the nearest neighbor rule. We propose a ..."
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Cited by 103 (4 self)
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Nearest neighbor classification assumes locally constant class conditional probabilities. This assumption becomes invalid in high dimensions with finite samples due to the curse of dimensionality. Severe bias can be introduced under these conditions when using the nearest neighbor rule. We propose
Uncertain Nearest Neighbor Classification
"... This work deals with the problem of classifying uncertain data. With this aim the Uncertain Nearest Neighbor (UNN) rule is here introduced, which represents the generalization of the deterministic nearest neighbor rule to the case in which uncertain objects are available. The UNN rule relies on the ..."
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in the presence of uncertainty, in that it correctly models the right semantics of the nearest neighbor decision rule when applied to the uncertain scenario. An effective and efficient algorithm to perform uncertain nearest neighbor classification of a generic (un)certain test object is designed, based
Svmknn: Discriminative nearest neighbor classification for visual category recognition
 in CVPR
, 2006
"... We consider visual category recognition in the framework of measuring similarities, or equivalently perceptual distances, to prototype examples of categories. This approach is quite flexible, and permits recognition based on color, texture, and particularly shape, in a homogeneous framework. While n ..."
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Cited by 333 (10 self)
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nearest neighbor classifiers are natural in this setting, they suffer from the problem of high variance (in biasvariance decomposition) in the case of limited sampling. Alternatively, one could use support vector machines but they involve timeconsuming optimization and computation of pairwise distances
Flexible Metric Nearest Neighbor Classification
, 1994
"... The Knearestneighbor decision rule assigns an object of unknown class to the plurality class among the K labeled "training" objects that are closest to it. Closeness is usually defined in terms of a metric distance on the Euclidean space with the input measurement variables as axes. The ..."
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Cited by 135 (2 self)
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The Knearestneighbor decision rule assigns an object of unknown class to the plurality class among the K labeled "training" objects that are closest to it. Closeness is usually defined in terms of a metric distance on the Euclidean space with the input measurement variables as axes
Nearest Neighbor Classification
, 2011
"... What is called supervised learning is the most fundamental task in machine learning. In supervised learning, we have training examples and test examples. A training example is an ordered pair 〈x, y 〉 where x is an instance and y is a label. A test example is an instance x with unknown label. The goa ..."
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Cited by 1 (0 self)
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→ Y. A supervised learning algorithm is not a classifier. Instead, it is an algorithm whose output is a classifier. Mathematically, a supervised learning algorithm is a higherorder function: it is a function of type (X × Y) n → (X → Y) where n is the cardinality of the training set. The nearestneighbor
Rates of Convergence for Nearest Neighbor Classification
"... Nearest neighbor methods are a popular class of nonparametric estimators with several desirable properties, such as adaptivity to different distance scales in different regions of space. Prior work on convergence rates for nearest neighbor classification has not fully reflected these subtle properti ..."
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Cited by 1 (0 self)
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Nearest neighbor methods are a popular class of nonparametric estimators with several desirable properties, such as adaptivity to different distance scales in different regions of space. Prior work on convergence rates for nearest neighbor classification has not fully reflected these subtle
Efficient Implementation of Nearest Neighbor Classification ⋆
"... Summary. An efficient approach to Nearest Neighbor classification is presented, which improves performance by exploiting the ability of superscalar processors to issue multiple instructions per cycle and by using the memory hierarchy adequately. This is accomplished by the use of floatingpoint arit ..."
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Summary. An efficient approach to Nearest Neighbor classification is presented, which improves performance by exploiting the ability of superscalar processors to issue multiple instructions per cycle and by using the memory hierarchy adequately. This is accomplished by the use of floating
Nearest neighbor classification for facies delineation
 Water Resour. Res
"... [1] Geostatistics has become the dominant tool for probabilistic estimation of properties of heterogeneous formations at points where data are not available. Ordinary kriging, the starting point in the development of other geostatistical techniques, has a number of serious limitations, chief among w ..."
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Cited by 3 (0 self)
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that is adequate for the task of facies delineation. Guided by the principle of parsimony, we identify nearestneighbor classification (NNC) as a viable alternative to geostatistics among deterministic techniques. We demonstrate that when used for the purpose of facies delineation, NNC, which has no fitting
Results 1  10
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227,367