Nearest Neighbor
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 ...
19
DISTRIBUTED NEAREST NEIGHBOR MATCHING ALGORITHM FOR COMPUTER VISION
... The nearest neighbor search problem can be defined as follows: given a set of points P = {p1, ...are nearest to q is performed ...k-nearest neighbor (k-DR) ...first neighbor but ...
8
Efficient discriminative learning of parametric nearest neighbor classifiers
... the analysis of the Gaussian kernel density estimation, and attempt to learn the prototypes for nearest neighbor search and the classifier parameters jointly and discriminatively. The decision boundary of ...
9
Pseudometrics for Nearest Neighbor Classification of Time Series Data
... Generalizing from Euclidean and spaces to metric and pseudometric spaces is somehow a sensible next step of development since metric spaces bear some relationship with spaces. Metric spaces are well studied. For example, ...
24
Approximate Nearest Neighbor: Towards Removing the Curse of Dimensionality
... the nearest neighbor presented here is a simplification of the reduction in [30] (which itself was much simpler and more efficient than the reduction in ...
30
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 ...
8
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 = ...
9
Nearest Neighbor Queries with Counting Aggregate-based Conditions
... With the increasing dissemination of devices to capture digital data, such as digital cameras, med- ical scanners, satellites and biomolecular machines, huge amounts of complex data are being gen- erated every day. In ...
16
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 ...
9
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 ...
5
Rate of Convergence of $k$-Nearest-Neighbor Classification Rule
... probability 0. For instance when the distribution µ of X has a density f, this assumption is satisfied. In any case, by adding a randomizing component to X one can ensure that this assumption holds. Choose an integer k ...
16
Clustering with shared nearest neighbor unscented transform based estimation
... Clustering is supposed as an unverified process in which the process of organizing objects into groups whose members are similar in some way. The authority of clustering results has to be evaluated by discovery the ...
10
Fast Nearest Neighbor Search with Keywords Using IR2-Tree
... the nearest restaurant that offers “steak, spaghetti, and brandy” all at the same ...“globally” nearest restaurant (which would have been returned by a traditional nearest neighbor query), but ...
10
LZW Compressed Text Classification using Nearest Neighbor Classifier
... At each execution of the basic step an acceptable input string ω has been parsed off. The next character K is read and the extended string ωK is tested to see if it exists in the string table. For each training document ...
5
Universal Morphological Analysis using Structured Nearest Neighbor Prediction
... (i) Nearest Neighbor: The training languages include all seven other languages in our data set, and the predictions with minimal distance to a training language are chosen (the nearest ...
11
A Nearest Neighbor Approach to the Automatic Analysis of Ancient Greek Morphology
... “nearest neighbor” of a word. At testing, given an inflected form, its neighbor is any word to which it can be transformed using the affix ...its nearest neighbor in the training set ( ...
8
Dimensionality reduction by minimizing nearest-neighbor classification error
... Methods based on finding the lower dimensional manifold in which the data lies are ISOMAP Tenenbaum et al. (2000) and Locally Linear Em- bedding (LLE) Roweis and Saul (2000); de Ridder et al. (2003), which are ...
22
Evaluating the Performance of Dual Weighted K Nearest Neighbor Classifier
... A non-parametric, very simple to use, effective instance-based learning algorithm called K-Nearest Neighbor (KNN), is most widely used to classify the objects in data mining. KNN has some shortcomings which ...
7
Parametric local metric learning for nearest neighbor classification
... Adaptive Nearest Neighbor classification [8], DANN, learns local metrics by shrinking neighborhoods in directions orthogonal to the local decision bound- aries and enlarging the neighborhoods parallel to ...
9
Distance Metric Learning for Large Margin Nearest Neighbor Classification
... The accuracy of k-nearest neighbor (kNN) classification depends significantly on the metric used to compute distances between different examples. In this paper, we show how to learn a Maha- lanobis distance ...
38