Nearest Neighbor (K-NN)
Pseudometrics for Nearest Neighbor Classification of Time Series Data
... -nearest neighbor ( -NN) still receives regular interest among researchers; both in the theoretical aspect and the practical ...k nearest neighbors of the sample being ...
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Rate of Convergence of $k$-Nearest-Neighbor Classification Rule
... is the minimax optimal rate of convergence for the class of regression functions D, which have Lipschitz continuous gradients, that is, they are differentiable and the partial derivatives are Lipschitz continuous. ...
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Nearest Neighbor Queries with Counting Aggregate-based Conditions
... k-Nearest Neighbor query (k-NNq), which returns the k elements most similar to a given reference ...basic nearest neighbor search, which would allow retrieving more relevant information to the ...
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DISTRIBUTED NEAREST NEIGHBOR MATCHING ALGORITHM FOR COMPUTER VISION
... K Nearest Neighbors (k-NN) search is a widely used category of algorithms with applications in domains such as computer vision, machine learning and data ...various nearest neighbor search techniques ...
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Nearest Neighbor Search with Anytime Clustering Method
... the nearest neighbors of a given data point is needed and mainly it depends upon clustering ...(Online Nearest-neighbor Estimation), k-nearest neighbor (kNN), CKNN (Continuous k ...
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A Short Term Traffic Flow Forecasting Method Based on a Three Layer K Nearest Neighbor Non Parametric Regression Algorithm
... K-nearest neighbor non parametric regression method is proved to be a reliable method for short-term traffic flow forecasting [3] [4], which can favorably reflect the traffic flow’s non-linearity, ...
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Nearest Neighbor Voting in High Dimensional Data: Learning from Past Occurrences
... Supervised learning (classification) is one of the most fundamental machine learning tasks, often encountered in various practical applications. It involves assigning a label to a new piece of input data, where the label ...
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When Is Nearest Neighbor Meaningful?
... [31] provides us with information on the performance of both the SS tree and the R* tree in finding the 20 nearest neighbors. Conservatively assuming that linear scans cost 15% of a random examination of the data ...
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A Nearest Neighbor Data Structure for Graphics Hardware
... Nearest neighbor search is a core computational task in database systems and throughout data analysis. It is also a major computational bottleneck, and hence an enormous body of research has been devoted to ...
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Efficient Surf Tracking by Nearest Neighbor Classifier
... Abstract—Visual object tracking is a fundamental problem in many real applications, like video security surveillance, human computer interaction (HCI), video communication and compression, augmented reality, traffic ...
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Nearest Neighbor Search with Keywords in Spatial Databases
... R-trees are tree data structures used for spatial access methods, i.e., for indexing multi-dimensional information such as geographical coordinates, rectangles and polygons. R-tree might be to store spatial objects such ...
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Keyword Search for Quick Finding of Nearest Neighbor
... It is observed that in the existing system, real nearest neighbor lies quite far away from the query point, while all the closer neighbors are missing at least one of the query keywords. IR2-tree has a few ...
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Approximate Nearest Neighbor: Towards Removing the Curse of Dimensionality
... the nearest neighbor problem has a solution with O(d O(1) log n) query time, but using n O(d) space ([46], building on ...same nearest neighbor in P, has complexity n Θ(d) ...
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OPRCP: approximate nearest neighbor binary search algorithm for hybrid data over WMSN blockchain
... In terms of index structure, for a single type of data, people performed well with approximate similarity search by constructing feature vector indexes. For example, for image data, there are two index structures ...
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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 H = ...
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A Novel Weighted Voting for K-Nearest Neighbor Rule
... k-nearest neighbor classifier is very sensitive to the selected value of ...the nearest neighbors vary widely in their distances and the closer ones more reliably indicate the class of the query ...
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Hubless Nearest Neighbor Search for Bilingual Lexicon Induction
... This paper presents a new method for Bilingual Lexicon Induction (BLI), which we call Hub- less Nearest Neighbor (HNN). BLI is the task of creating a lexicon of translation equivalents such as, bank:banc or ...
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Video Inpainting using Nearest Neighbor Field Algorithm
... In our proposed video inpainting approach entire video Sequence is analyzed, after that masking algorithm is applied. once the damaged frames detected then we used nearest neighbor algorithm to find best ...
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Analysis on Fast Nearest Neighbor Search with Keywords
... Spatial queries with keywords have not been extensively explored. In the past years, the community has sparked enthusiasm in studying keyword search in relational databases. It is until recently that attention was ...
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Quick Search of the Nearest Neighbor with Words
... It is until recently that attention was diverted to multidimensional data. The best method to date for nearest neighbor search with keywords is due to Felipe et al.. They nicely integrate two well-known ...
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