1-nearest neighbor method
Nearest Neighbor Search with Anytime Clustering Method
... k nearest neighbors is ...K nearest neighbors on large-scale datasets. DIMO presents a novel method for partitioning, searching, and storing high-dimensional datasets on distributed infrastructures ...
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DETECTION OF HIGH IMPEDANCE FAULTS IN DISTRIBUTION FEEDERS - K-NEAREST NEIGHBOR METHOD
... 612 | P a g e conductor is not disconnected but only is to be connected to a high impedance object (such as tree branches and leaves). The HIFs are usually associated with an electric arc, which may cause a fire. Due to ...
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A Semantic Distance Based Nearest Neighbor Method for Image Annotation
... Image annotation (or classification) and retrieval has drawn considerable attention in both research and industrial fields. Finding relevant images from web and other large-scale databases is not a trivial task because ...
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Evaluation Accuracy of Nearest Neighbor Sampling Method in Zagross Forests
... This research was investigated in the chahartagh forest reserve, Ardal region, chaharmehal and Bakhtiari Province, Central Zagros forest, and southwest Iranian state (Figure 1). Chahartagh Forest reserve located ...
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Comparative Analysis of K-Nearest Neighbor and Modified K-Nearest Neighbor Algorithm for Data Classification
... validation and it may be bigger if k-fold is larger depending on requirement [7], however according to Last (2006) there is no exact terms on determining how many k-fold is needed, it may be 33%, 50%, and 60% of the data ...
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1. Spatial data mining for finding nearest neighbor and outlier detection
... k-nearest neighbor algorithm for top-n query processing in spatial ...points nearest to the query ...a method to find candidate region which shortens the search ...searching nearest ...
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Nearest neighbor search with multiple random projection trees : core method and improvements
... always 1, we control the number of leaves chosen from that tree (chosen from the tree similarly to our priority search strategy in section ...to 1 for (sparse) RP-trees, the sparsity parameter was fixed to ...
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The Mutual Nearest Neighbor Method in Functional Nonparametric Regression
... Consider the three functions D (black line), Z (blue dashed) and [ (red twodash) presented in Figure 1. Intuitively, the general appearance of D and Z is almost identical while D and [ are different. Based on the ...
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An Infectious Disease Prediction Method Based on K-Nearest Neighbor Improved Algorithm
... Firstly, each type of training sample set is clustered separately, and then the sample near the center point of the cluster is used as a subclass of the category to represent all the samples of the category, so that the ...
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Sparse Linear Discriminant Analysis with Applications to High Dimensional Low Sample Size Data
... the nearest centroid, 1-nearest neighbor and support vector machine, have been applied to the re- duced data for ...The nearest centroid method has the best overall ...
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Nearest neighbor imputation algorithms: a critical evaluation
... > 1 [11]. In the nearest-variable procedure (kNN-V) and variants (kNN-H and kNN-A) described in [12] k rele- vant features are selected with respect to the variable with missing values by means of ...
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On the Rate of Convergence of the Bagged Nearest Neighbor Estimate
... bagged nearest neighbor rule then? The answer is ...the 1-NN is a very popular technique for regression and classification in the machine learning community, and most—if not all—empirical studies ...
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A Short Term Traffic Flow Forecasting Method Based on a Three Layer K Nearest Neighbor Non Parametric Regression Algorithm
... regression method, non-parametric regression method sets no strict limits on the data and describes the system based on the sufficient historical ...regression method, the relation- ship between ...
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Survey on Classification and Prediction Approaches in Traffic Flow
... K-nearest neighbor has nonparametric, small error ratio and good error ...k-nearest neighbor prediction model is to build a representative historical database with large ...close ...
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Convergence of Multi-pass Large Margin Nearest Neighbor Metric Learning
... which implies that a global optimum can be found [12, 2]. However, this de- sirable property only holds under the assumption that the closest k neighbors from the same class - the so-called target neighbors - remain ...
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Supervised text classification of medical triage reports
... Classification method kNN is used by Huang (2011)[7] to retrieve similar documents and extract possible classes for a target ...of neighbor documents in which the class occurred and summing the document ...
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Züfle, Andreas (2013): Similarity search and mining in uncertain spatial and spatio-temporal databases. Dissertation, LMU München: Fakultät für Mathematik, Informatik und Statistik
... In this section, an effective and efficient probabilistic pruning criterion for k-Nearest Neigh- bor Queries queries on uncertain data is presented. This criterion allows to assess the prob- ability that an ...
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Interaction between Substitutional and Interstitial Elements in α iron Studied by First principles Calculation
... fifth nearest neighbor sites differ little for the 3d transition metal elements, and are rather small, that is, less than 0:3 10 19 ...second nearest neighbor ...fifth nearest ...
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An Augmentation of Data Collection Functioning With Implementation by Pattern Variation Discovery in Wireless Sensor Networks: Novel Overview with Applications
... This Wireless Sensor Networks (WSNs) is depends on a simple equation: Sensing + CPU + Radio = Thousands of achievable applications. Wireless sensor network is a type of wireless network. It is minor and organization ...
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Recommendation of online Products Using Microblogging Information in Social Media
... recommended method to over the cross site cold start ...tree method where a bridge is created among e-commerce and social networking sites using some connected ...this method is effective for cross ...
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