Fast Design of Reduced-Complexity Nearest ... - Semantic Scholar
Recommend Documents
2056 matches - We propose two solutions for both nearest neigh- bors and ... the parameters in a learning stage adopting
Aug 4, 2011 - This paper presents a novel approach for accelerating the popular Reciprocal Nearest Neighbors (RNN) clustering algorithm,. i.e. the fast-RNN.
Jan 11, 2011 - BUSINESS, ... }. ... said to be small, and nearest-neighbor methods may work well. .... bookkeeping calculations involving lower bounds that are scalars. ..... http://research.microsoft.com/en-us/um/people/minka/software/.
increase the speed of basic kNN algorithm. II. NEAREST NEIGHBOR ... that covers large number of neighbors and a data tuple is located with largest global ...
Computer Architecture Department, Universitat Polit`ecnica de Catalunya, ... programs allowing an efficient use of the data stored in the cache memory. 1 Introduction. The Nearest Neighbor (NN) classification procedure is a popular technique ..... Da
The results obtained are equivalent to a K Nearest Neighbor classifier (KNN) for maximum ... topological neighbors in the graph toward the input signal. For an ...
computer vision, image retrieval, data mining, etc. The prob- ... We build a k-nearest neigh- bor (k-NN) ... A k-NN graph is built from dataset points and when queried with a ... neighbors, a backtrack step from the leaf node is performed and the ...
Abstract: Conventional spatial queries, such as range search and nearest neighbor retrieval, involve only conditions on objects' Geometric properties. Today ...
We introduce a new nearest neighbor search al- gorithm. The algorithm builds a nearest neighbor graph in an offline phase and when queried with a new point ...
whether the number of prototypes found is user-defined or ''automatic.'' Generalization error rates of the ... classes. The standard nearest prototype 1-np classification rule assigns x to the. Ž class of the ''most ..... Editing tech- niques are of
fly toward better and better search areas over the course of the search process ... Particle swarm optimization was utilized to optimize a nearest neighbor ...... [15] X. Li, H. Yan, and J. Wang, Search Engine: Principle, Technology and. Systems.
We extend soft nearest neighbor classification to fuzzy classification with ..... By this threshold we obtain a sufficiently good labeling after. 300 complete steps.
an new song chooses the nearest neighbor from amongst these to ... that while our approach is better than random, it does not compare well with ... This looked at artist classification of. 30 and 40 ... characteristics such as voice and typical instr
water to penetrate into the tablets, along with directly compressible mannitol to enhance mouth feel. The tablets were evaluated for hardness, friability, thickness, ...
disintegrants by formulating cefixime dispersible tablets. Indian J Pharm. Edu Res 2005;39:194-7. 12. Khan KA. The concept of dissolution efficiency. J Pharm ...
Oct 15, 2014 - ... Engineering âG. Marconiâ, University of Bologna, Cesena (FC), Italy .... I A,C. Variable Nodes. Check Nodes. Decoding path for 10 iterations.
the loads Xd+1 for the next day d + 1, WNN first finds the K nearest neighbors of the previous day Xd; then, the prediction for the new day is a weighted linear ...
Pasadena, CA 91125, USA. {ronen.basri, tal.hassner}@weizmann.ac.il [email protected]. Abstract. Linear and affine subspaces are commonly used to de-.
an Empirical Risk Minimization principle, in an effort to control the performance on future (yet to be observed) data. The con- struction of such a performance ...
School of Computer Sciences, University of Birmingham. Birmingham B15 2TT ... of the complete training set in which some of the training patterns are excluded, in order to reduce the ... of a genetic algorithm for designing edited k-nearest neighbor
Dec 12, 2013 - apel sebelum apel pagi dimulai' (they eat an apple before the morning assembly) should be ..... [12] Hasan A, Soenjono D, Hans L, Anton MM.
Here, we propose to use another such classifier, called Nearest Convex Hull. (NCH) classifier. ... See Figure 1 for an illustrative binary classification example.
rows, with many different missingness patterns and large proportions of missing data. .... number of rows in the data matrix [12] and [13], i.e. the algorithm execution time )( .... which gives the first row number of the pattern in the data matrix.
Let the graph Gk = (1..k, Ek) denote the Delaunay triangulation of points. 1..k. Nearest neighbor search with respect to points 1..k. 1We use a..b as a shorthand ...
Fast Design of Reduced-Complexity Nearest ... - Semantic Scholar
Eel-Wan Lee and Soo-Ik Chae. AbstractâIn this paper, we propose a method of designing a reduced complexity nearest-neighbor (RCNN) classifier with near- ...
562
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, VOL. 20, NO. 5, MAY 1998
Fast Design of Reduced-Complexity Nearest-Neighbor Classifiers Using Triangular Inequality Eel-Wan Lee and Soo-Ik Chae Abstract—In this paper, we propose a method of designing a reduced complexity nearest-neighbor (RCNN) classifier with near-minimal computational complexity from a given nearest-neighbor classifier that has high input dimensionality and a large number of class vectors. We applied our method to the classification problem of handwritten numerals in the NIST database. If the complexity of the RCNN classifier is normalized to that of the given classifier, the complexity of the derived classifier is 62 percent, 2 percent higher than that of the optimal classifier. This was found using the exhaustive search. Index Terms—Nearest-neighbor classifier, triangular inequality, computational complexity, NIST database, fast design.
———————— F ————————
1 INTRODUCTION THE nearest-neighbor (NN) classifier with representation vectors ci , for a given input x, calculates the distance from ci to x for all i
