Where results make sense
 About us   |   Why use us?   |   Reviews   |   PR   |   Contact us

# Topic: Nearest neighbour algorithm

 Wikipedia: Nearest neighbour algorithm The nearest neighbour algorithm was one of the first algorithms used to determine a solution to the traveling salesman problem, and usually comes to within twenty percent of the optimal route. The nearest neighbour algorithm is easy to implement and can be done quickly, but it can sometimes miss shorter routes which are easily noticed with human hindsight. The results of the nearest neighbour algorithm should be checked before use, just in case such a shorter route has been missed. www.factbook.org /wikipedia/en/n/ne/nearest_neighbour_algorithm.html   (239 words)

 nearest neighbour algorithm - Article and Reference from OnPedia.com The nearest neighbour algorithm was one of the first algorithms used to determine a solution to the traveling salesman problem, and usually comes to within twenty percent of the optimal route. The nearest neighbour algorithm is easy to implement and can be done quickly, but it can sometimes miss shorter routes which are easily noticed with human hindsight. The results of the nearest neighbour algorithm should be checked before use, just in case such a shorter route has been missed. www.onpedia.com /encyclopedia/nearest-neighbour-algorithm   (250 words)

 Nearest Neighbour   (Site not responding. Last check: 2007-10-25) Visit the nearest city to city 'a', which we shall call city 'b'. As per point 3, repeatedly visit the nearest unvisited city to the current city until all cities have been visited once. Once again,draw a line between the two, then locate the nearest unvisited city to the newly incorporated city of the previous step. users.cs.cf.ac.uk /C.L.Mumford/howard/NearestNeighbour.html   (129 words)

 Geomantics - 3D, GIS, Landscape Visualization, Graphics and Business Software This version of GenesisII offers two interpolation algorithms, an implementation of the Natural Neighbour algorithm (nngridr), which should be used to interpolate the majority of missing heights, and a trend analysis/smoothing transform, which should be used for filling in the last few points once the majority of heights have been interpolated. Nearest neighbour interpolation should always be used except for final 'point filling' as trend analysis often produces artifactes when used other than with small groups of points. In some circumstances the nearest neighbour interpolation algorithm will be unable to proceed and an error message will be displayed: for example if the height to width ration of the selected area is too extreme. www.geomantics.com /tutorial14.htm   (1129 words)

 Nearest neighbour algorithm - Encyclopedia, History, Geography and Biography This article is about an approximation algorithm to solve the travelling salesman problem. For other uses, see nearest neighbor. The nearest neighbour algorithm was one of the first algorithms used to determine a solution to the travelling salesman problem. The nearest neighbour algorithm is easy to implement and executes quickly, but it can sometimes miss shorter routes which are easily noticed with human insight. www.arikah.com /encyclopedia/Nearest_neighbour_algorithm   (253 words)

 Citations: New formulation and improvements of the Nearest-Neighbour Approximating and Eliminating Search Algorithm - ... In the present work, a branch and bound algorithm is proposed that searches the nearest vectors in a vector space where the dissimilarity between two vectors is expressed by the euclidean distance. One of these algorithms is LAESA [4] which nds the nearest neighbour to a given sample, while maintaining the average number of distance.... One of these algorithms is the LAESA [4] which nds the nearest neighbour to a given sample, while maintaining the average number of.... citeseer.ist.psu.edu /context/455498/0   (1838 words)

 Nearest neighbour algorithm   (Site not responding. Last check: 2007-10-25) The nearest neighbour algorithm was one of the first algorithms used to determine a solution to traveling salesman problem and usually comes to within twenty of the optimal route. The nearest neighbour algorithm is easy to and can be done quickly but it sometimes miss shorter routes which are easily with human hindsight. The results of the neighbour algorithm should be checked before use in case such a shorter route has missed. www.freeglossary.com /Nearest_neighbor_algorithm   (383 words)

 Nearest neighbour algorithm : Information and resources about Nearest neighbour algorithm : School Work Guru Pick the node which is closest to the last node which was placed in set A and is not in set A; put this closest neighbouring node into set A Repeat step 3 until all nodes are in set A and B is empty. To be precise, for every constant r there is an instance of the traveling salesman problem such that the length of the tour computed by the nearest neighbour algorithm is greater than or equal to r times the length of the optimal tour. www.schoolworkguru.org /encyclopedia/n/ne/nearest_neighbour_algorithm.html   (307 words)

 4 Use the k-nearest neighbour algorithm on the digit data set as follows: Learn in increments of 50 (you should get 8 result sets in all) for each value of k. Since as the number of neighbours are increasing the farther instances will also effect the output that is the reason the output accuracy is decreaing. So the K -nearest Neighbour algorithm will take less time in training but it will take more for giving answer to a query instance as complement to the Backprop networks. www.cse.iitk.ac.in /users/suryaksp/K_nearest_neighbour_algo.htm   (528 words)

 [No title] The agglomerative approach to cluster analysis, used by the nearest and farthest neighbour algorithms, is a bottom-up clumping approach where we begin with n singleton clusters and successively merge clusters to produce the other ones. Also, if the algorithm is allowed to run until all clusters are connected then a spanning tree is generated, or more precisely, a minimal spanning tree since the edges we are inserting between clusters are always the shortest ones in distance. The farthest-neighbour algorithm prevents the grouping of elongated clusters; instead, clusters are composed of complete subgraphs (Figure 4). cgm.cs.mcgill.ca /~soss/cs644/projects/siourbas/sect5.html   (877 words)

 BioMed Central | Full text | TMB-Hunt: An amino acid composition based method to screen proteomes for beta-barrel ... A major advantage of this algorithm is that it is robust to noisy data (given a large dataset), as taking the weighted average of the nearest neighbours smoothes out isolated training instances. Statistical chance means that the k-nearest neighbour sets tend to contain more proteins from the dominant class, leading to this class as the dominant prediction even in the presence of substantial evidence for membership of one the other classes in the nearest neighbour set. Genetic algorithms are an optimisation approach, based on Darwinian principles, which assume that given a population of individuals, environmental pressures cause natural selection thus increasing the overall fitness of the population [43]. www.biomedcentral.com /1471-2105/6/56   (7046 words)

 Functional classification of proteins using a nearest neighbour algorithm The nearest neighbour rule [Cover and Hart, 1967; Dasarathy, 1991] states that a test instance is classified according to the classifications of "nearby" training examples. The performance of the nearest neighbour algorithm heavily depends on the properties of this function. Otherwise, the algorithm takes all entries that have at least a similarity score 0.9 times as high as the best score and checks if at least 50% of them are assigned to the same class. www.bioinfo.de /isb/2003/03/0023/main.html   (4718 words)

 K-nearest neighbor algorithm In pattern recognition, the k-nearest neighbor algorithm (k-NN) is a method for classifying objects based on closest training examples in the feature space. The accuracy of the k-NN algorithm can be severely degraded by the presence of noisy or irrelevant features, or if the features scales are not consistent with their relevance. The algorithm is easy to implement, but it is computationally intensive, especially when the size of the training set grows. www.1bx.com /en/K-Nearest_Neighbor_algorithm.htm   (496 words)

 Nearest Neighbours   (Site not responding. Last check: 2007-10-25) The nearest neighbour algorithm is a very simple, yet relatively powerful, technique. nearest neighbour) belongs to each class is calculated. When employing the nearest neighbour algorithm, two choices must be made - the value of www.srcf.ucam.org /~hmw26/coursework/dme/node17.html   (133 words)

 Team Sergei   (Site not responding. Last check: 2007-10-25) We modify the classical algorithm to use training examples of the form: (ObjectId,Red,Green,Blue),where ObjectId is a value that relates to the object: 1=Red, 2=Green, 3=Blue, 4=Yellow. Once we have determined the k nearest neighbours, a vote is taken between them using the ObjectId to determine how to classify the query point. Four of the points nearest in the colour space to the query point are red and one is yellow so the voting is Red 4 - 1 Yellow. www.cs.bham.ac.uk /resources/courses/robotics/halloffame/2005/team3/colour.htm   (310 words)

 ECS EPrints Service - Dimensionality Reduction and Representation for Nearest Neighbour Learning An increasing number of intelligent information agents employ Nearest Neighbour learning algorithms to provide personalised assistance to the user. The importance of discarding irrelevant terms from the documents is then addressed, and this is generalised to examine the behaviour of the Nearest Neighbour learning algorithm with high dimensional data sets containing such values. The thesis concludes with a discussion of ways in which attribute selection and dimensionality reduction techniques may be used to improve the selection of relevant attributes, and thus increase the reliability and predictive accuracy of the Nearest Neighbour learning algorithm. eprints.ecs.soton.ac.uk /7788   (326 words)

 Imaging   (Site not responding. Last check: 2007-10-25) Identifying the nearest neighbour of a given obervation vector from a set of training vectors is conceptually straightforward with n distance calculations to be performed. The algorithm is a linear approximation called LAESA (linear approximation and elimination algorithm), and it reduces a training dataset to a set of base prototypes. The LAESA algorithm begins by first selecting a base prototype b1, arbitrarily from the set of prototypes and the distance to every member of the remaining prototypes is calculated and stored in an array A. fuzzy.iau.dtu.dk /smear/imaging.html   (327 words)

 Cogprints - Corpus-based Learning of Analogies and Semantic Relations   (Site not responding. Last check: 2007-10-25) We present an algorithm for learning from unlabeled text, based on the Vector Space Model (VSM) of information retrieval, that can solve verbal analogy questions of the kind found in the SAT college entrance exam. The VSM algorithm correctly answers 47% of a collection of 374 college-level analogy questions (random guessing would yield 20% correct; the average college-bound senior high school student answers about 57% correctly). We use a supervised nearest-neighbour algorithm that assigns a class to a given noun-modifier pair by finding the most analogous noun-modifier pair in the training data. cogprints.org /4518   (278 words)

 1 Either only the best neighbour is considered or a number of nearest neighbours vote for the classification of the new data item. The nearest neighbour model can be used as a classifier, but it does not explain its chooses like e.g. It should be added that nearest neighbour have the ability to create complex decision boundaries for numeric attributes e.g. www.hi.is /~benedikt/Courses/DataMining7.htm   (5002 words)

 Nearest neighbour algorithm for predicting protein subcellular location by combining functional domain composition and ...   (Site not responding. Last check: 2007-10-25) Nearest neighbour algorithm for predicting protein subcellular location by combining functional domain composition and pseudo-am Nearest neighbour algorithm for predicting protein subcellular location by combining functional domain composition and pseudo-am Very high success rates were observed, suggesting that such a hybrid approach may become a useful high-throughput tool in the area of bioinformatics and proteomics. www.nodalpoint.org /node/904   (129 words)

 Performance of k-nearest neighbours algorithm   (Site not responding. Last check: 2007-10-25) We implemented three diffrent variations of the k-nearest neighbours algorithm and here we present som results. The first variant uses an euclidian distance to calculate the k nearest neigbours and takes the most common class as the answer. To test our segmentation algorithm we wrote a sample page of numbers and we also tested the k-nearest neighbour algorithm on that page. www.dtek.chalmers.se /~d95danb/ocr/k_nearest_result.html   (492 words)

 Capturing Interest Through Inference and Visualization: Ontological User Profiling in Recommender Systems Classifiers like k-Nearest Neighbour allow more training examples to be added to their term-vector space, without the need to re-build the entire classifier, and they degrade well, returning classes in the right “neighbourhood” and hence at least partially relevant. There is a clear benefit from both types of bootstrapping algorithm, made possible because the profiles are represented using ontological terms and hence profile interests can be mapped to the external bootstrapping ontology. The profiling algorithm, shown in figure 10, adds error adjustment values for every day under the feedback interest bar to constrain interest values to those given in the profile feedback. www.ecs.soton.ac.uk /~sem99r/kcap2003.html   (5207 words)

 nrich.maths.org::Mathematics Enrichment::Travelling Salesman There are some algorithms for this type of problem that can help. I'll apply the Nearest Neighbour Algorithm, and compare the result with the counted distances. Using the Nearest Neighbour Algorithm I have found a minimum distance of 480 units. www.nrich.maths.org /public/viewer.php?obj_id=2325&refpage=viewer.php&part=solution&nomenu=1   (1029 words)

 PASCAL - Nearest neighbour search is one of the most simple and used technique in Pattern Recognition. One of the most known fast nearest neighbour algorithms was proposed by Fukunaga and Narendra. The algorithm builds a tree in preprocess time that is traversed on search time using some elimination rules to avoid its full exploration. eprints.pascal-network.org /archive/00001567   (146 words)

 Cluster Analysis In order to determine the number of clusters and their partitions, numerous clustering algorithms exist which fall in one of two categories: hierarchical and non-hierarchical clustering. Non-hierarchical clustering algorithms produce disjoint clusters and thus work well when a given set is composed of a number of distinct classes or when the data description is "flat". Since using different clustering criterion and methods for determining the number of clusters and their partitions can produce varying results when applied to identical data, we should be wary about accepting the results of a single clustering method. cgm.cs.mcgill.ca /~soss/cs644/projects/siourbas/cluster.html   (2676 words)

 [No title] Though this is not the focus of Robinson's study, it would also be useful to report how long the training took (measured in pattern presentations or with a rough count of floating-point operations required) and what level of success was achieved on the training and testing data after various amounts of training. It is interesting that, for this problem, the nearest neighbour clasification outperforms any of the connectionist models. The best results were achieved with nearest neighbour analysis which classifies an item as the class of the closest example in the training set measured using the Euclidean distance. www-stat.stanford.edu /~tibs/ElemStatLearn/datasets/vowel.info   (1910 words)

 Test results   (Site not responding. Last check: 2007-10-25) Table 1 presents their results for many well-known learning algorithms, along with the results of running the VSM algorithm on the same data. In applying VSM learning, it is important that only neighbours produced by different speakers from the center input are accessed during training, as otherwise the weights will be optimized to recognize each vowel based on data from the same speaker. In fact, the nearest neighbour algorithm performed very well for this task, and the reason for this is shown by the fact that the feature weights changed only a little from their initial value of 1.0 during VSM learning. www.cs.ubc.ca /spider/lowe/papers/neural95/node6.html   (1116 words)

 Graph theory   (Site not responding. Last check: 2007-10-25) The development of algorithms to handle graphs is therefore of major interest in computer science. A Graph structure can be extended by assigning a weight to each edge, or by making the edges to the Graph directional (''A'' links to B, but B does not necessarily link to A, as in webpages), technically called a digraph. The data structure used depends on the Graph structure and the algorithm used for manipulating the graph. graph-theory.iqnaut.net   (1045 words)

 implementing K Nearest Neighbour algorithm using Managed P..   (Site not responding. Last check: 2007-10-25) I passed through the Tutorial and I would like to implement my own K nearest neighbour algorithm. But in K Nearest Neighbour algorithm there is no model to build until test data is present. We can say the model is actually training set of cases and parameter k (k nearest neighbours) In predicition phase euclidian distances should be calculated and predicitons has to be made depending on k lowest euclidian distances. www.dbforumz.com /implementing-Nearest-Neighbour-algorithm-Managed-PLUG-ftopict314805.html   (878 words)

Try your search on: Qwika (all wikis)

About us   |   Why use us?   |   Reviews   |   Press   |   Contact us