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PrivacyPreserving Nearest Neighbor Methods
"... COMPARING two signals is one of the most essential and prevalent tasks in signal processing. A large number of applications fundamentally rely on determining the answers to the following two questions: (1) How should two signals be compared? (2) Given a set of signals and a query signal, which signa ..."
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Cited by 8 (5 self)
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signals are the nearest neighbors of the query signal, i.e., which signals in the database are most similar to the query signal? The nearest neighbor (NN) search problem is defined as follows: Given a set S containing points in a metric space M, and a query point x ∈ M, find the point in S that is closest
Pairwise Nearest Neighbor Method Revisited
, 2004
"... The pairwise nearest neighbor (PNN) method, also known as Ward's method belongs to the class of agglomerative clustering methods. The PNN method generates hierarchical clustering using a sequence of merge operations until the desired number of clusters is obtained. This method selects the clust ..."
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Cited by 4 (0 self)
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The pairwise nearest neighbor (PNN) method, also known as Ward's method belongs to the class of agglomerative clustering methods. The PNN method generates hierarchical clustering using a sequence of merge operations until the desired number of clusters is obtained. This method selects
Acceleration of Binning Nearest Neighbor Methods
, 2000
"... A new solution method to the Nearest Neighbour Problem is presented. The method is based upon the triangle inequality and works well for small point sets, where traditional solutions are particularly ineffective. Its performance is characterized experimentally and compared with kd tree and Elias a ..."
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A new solution method to the Nearest Neighbour Problem is presented. The method is based upon the triangle inequality and works well for small point sets, where traditional solutions are particularly ineffective. Its performance is characterized experimentally and compared with kd tree and Elias
A ReExamination of Text Categorization Methods
, 1999
"... This paper reports a controlled study with statistical significance tests on five text categorization methods: the Support Vector Machines (SVM), a kNearest Neighbor (kNN) classifier, a neural network (NNet) approach, the Linear Leastsquares Fit (LLSF) mapping and a NaiveBayes (NB) classifier. We f ..."
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Cited by 853 (24 self)
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This paper reports a controlled study with statistical significance tests on five text categorization methods: the Support Vector Machines (SVM), a kNearest Neighbor (kNN) classifier, a neural network (NNet) approach, the Linear Leastsquares Fit (LLSF) mapping and a NaiveBayes (NB) classifier. We
Profilebased nearest neighbor method for pattern recognition
 J Korean Phys Soc
"... We propose a nearest neighbor method of pattern recognition which is based on a weighted distance measure between patterns derived from profiles. There are a few new ingredients to the proposed method, compared to the conventional nearest neighbor methods. The distance measure is defined as a weight ..."
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Cited by 5 (1 self)
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We propose a nearest neighbor method of pattern recognition which is based on a weighted distance measure between patterns derived from profiles. There are a few new ingredients to the proposed method, compared to the conventional nearest neighbor methods. The distance measure is defined as a
Comparison of discrimination methods for the classification of tumors using gene expression data
 JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
, 2002
"... A reliable and precise classification of tumors is essential for successful diagnosis and treatment of cancer. cDNA microarrays and highdensity oligonucleotide chips are novel biotechnologies increasingly used in cancer research. By allowing the monitoring of expression levels in cells for thousand ..."
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Cited by 770 (6 self)
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gene expression data is an important aspect of this novel approach to cancer classification. This article compares the performance of different discrimination methods for the classification of tumors based on gene expression data. The methods include nearestneighbor classifiers, linear discriminant
NEIGHBORSIGNIFICANCE KNEAREST NEIGHBOR METHOD
"... In this paper, we use NeighborSignificance KNearest Neighbor (NSKNN) algorithm to deals with the problem that " The file is classified automatically ". Simultaneously, we design many kinds of different characteristic choose experiment of tactics, in order to prove different chara ..."
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In this paper, we use NeighborSignificance KNearest Neighbor (NSKNN) algorithm to deals with the problem that " The file is classified automatically ". Simultaneously, we design many kinds of different characteristic choose experiment of tactics, in order to prove different
A nearest neighbor method for efficient ICP
 In Proc. 3DIM
, 2001
"... A novel solution is presented to the Nearest Neighbor Problem that is specifically tailored for determining correspondences within the Iterative Closest Point Algorithm. The reference point set P is preprocessed by calculating for each point ~p i 2 P that neighborhood of points which lie within a ..."
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Cited by 42 (2 self)
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constraint, called the Spherical Constraint, then the nearest neighbor falls within the neighborhood of the estimate. A novel theorem, the Ordering Theorem, is presented which allows the Triangle Inequality to efficiently prune points from the sorted neighborhood from further consideration. The method has
An algorithm for finding best matches in logarithmic expected time
 ACM Transactions on Mathematical Software
, 1977
"... An algorithm and data structure are presented for searching a file containing N records, each described by k real valued keys, for the m closest matches or nearest neighbors to a given query record. The computation required to organize the file is proportional to kNlogN. The expected number of recor ..."
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Cited by 764 (2 self)
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An algorithm and data structure are presented for searching a file containing N records, each described by k real valued keys, for the m closest matches or nearest neighbors to a given query record. The computation required to organize the file is proportional to kNlogN. The expected number
Multitask Learning,”
, 1997
"... Abstract. Multitask Learning is an approach to inductive transfer that improves generalization by using the domain information contained in the training signals of related tasks as an inductive bias. It does this by learning tasks in parallel while using a shared representation; what is learned for ..."
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Cited by 677 (6 self)
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demonstrate multitask learning in three domains. We explain how multitask learning works, and show that there are many opportunities for multitask learning in real domains. We present an algorithm and results for multitask learning with casebased methods like knearest neighbor and kernel regression
Results 1  10
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