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Topic: Nearest neighbor (pattern recognition)

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Nearest neighbour algorithm

Supervised learning

Machine learning

Unsupervised learning

Statistical classification

Linear discriminant analysis

Data mining

Maximum likelihood

Maximum likelihood estimation

	Nearest neighbor (pattern recognition) - Wikipedia, the free encyclopedia
		The nearest neighbor algorithm in pattern recognition is a method for classifying phenomena based upon observable features.
		Other variations of the algorithm include the k-nearest neighbor algorithm where several of the nearest feature vectors are computed, and the classification is made with the highest confidence only if all of the nearest neighbors are of the same class.
		As the amount of data approaches infinity, nearest neighbor is guaranteed to yield an error rate no worse than twice the Bayes error rate (the minimum achievable error rate given the distribution of the data).
	en.wikipedia.org /wiki/Nearest_Neighbor_(Pattern_Recognition) (382 words)

	Nearest neighbour algorithm - Wikipedia, the free encyclopedia
		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 20% of the optimal route.
		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.
		The result of the nearest neighbour algorithm should be checked before use, just in case such a shorter route has been missed.
	en.wikipedia.org /wiki/Nearest_neighbour_algorithm (267 words)

	Breakout Bulletin - February 2004
		In statistical pattern recognition, for example, the objective is to design a "classifier," which discriminates between the patterns you're trying to identify and all other patterns encountered by the classifier.
		Nearest neighbor estimates are local estimates, like a simple moving average, centered around the point of interest and recalculated using different neighbors at every other point of interest.
		I didn't try more than 50 nearest neighbors, but as this number approaches the length of the look back period, the prediction will be based on a greater and greater fraction of the look back period until the prediction is nothing more than an average of all price changes in the look back period.
	www.breakoutfutures.com /Newsletters/Newsletter0204.htm (2572 words)

	On the Finite Sample Performance of the Nearest Neighbor Classifier -- from Mathematica Information Center
		The finite sample performance of a nearest neighbor classifier is analyzed for a two-class pattern recognition problem.
		The statistical setup assumes that the pattern classes arise in nature with fixed a priori probabilities and that points representing the classes are drawn from Euclidean n-space according to fixed class-conditional probability distributions.
		For a family of smooth class-condition distributions characterized by asymptotic expansions in general form, it is shown that the m-sample risk Rm has a complete asymptotic expansion...where Rinfinity denotes the nearest neighbor risk in the infinite sample limit and the coefficients ck are distribution-dependent constants independent of the sample size m.
	library.wolfram.com /infocenter/Articles/2871 (228 words)

	Definition of Pattern recognition
		Pattern recognition (also known as classification or pattern classification) is a field within the area of computer science and can be defined as "the act of taking in raw data and taking an action based on the category of the data" [1].
		Pattern recognition is typically an intermediate step in a longer process.
		Pattern recognition itself is primarily concerned with the classification step.
	www.wordiq.com /definition/Pattern_recognition (434 words)

	Thesis.style
		Segmentation and preliminary recognition of madrigals notated in white mensural notation.
		The condensed nearest neighbor rule using the concept of mutual nearest neighborhood.
		Recognition of musid using the special image-input-device enabling to scan the staff of music as the supporting system for the blind (in Japanese).
	www.music.mcgill.ca /~ich/research/omr_ich_ref.html (1780 words)

	CS 396 Pattern Recognition
		Pattern Recognition techniques are useful in many applications of computer science and information systems, such as information retrieval, data mining, artificial intelligence and image processing.
		This course is an introduction to the foundation of pattern recognition algorithms.
		Topics to be studied: data structures for pattern representation, feature extraction and selection, parametric and non-parametric classification, supervised and non-supervised learning, clustering, decision trees, nearest neighbor, artificial neural networks and hidden Markov models.
	csis.pace.edu /~scha/PR396_F02 (172 words)

	Pattern Recognition
		New patterns are then classified by a simple rule: the same class as whichever prototype the unknown pattern is closes to in the feature space.
		All patterns in one region are nearest to that region's class than to any other's.
		This can be thought of as a three-class, three-feature pattern recognition problem, classifying each pixel as one of the three classes according to the values in the three different flavors of MRI.
	rivit.cs.byu.edu /morse/550-F95/node34.html (1596 words)

	Pattern Recognition, Winter 2002-3
		Pattern recognition consists of two principal aspects, extraction of d numerical features from the unknown objects and then performing decision making in the d-dimensional space.
		Construct nearest neighbor classifiers using the single nearest neighbor and the three nearest neighbors (3-NN) to classify the points according to two categories.
		Replace the training sets for the nearest neighbor methods with significantly smaller sets, re-perform the experiments, determine the time speed-up, and the classification accuracy loss.
	www.cs.rit.edu /~pga/2002_2_PR_outline.htm (1064 words)

	The Pattern Recognition Basis of AI
		Pattern Recognition II In this chapter the pattern recognition problem becomes a problem in abstract math.
		If you consult the "pattern recognition" journals about all you will find is a nearly endless supply of abstract math and theorem proving and hardly any practical applications of all of it.
		In the nearest neighbor algorithm you have to keep a large inventory of patterns and their classifications so searching through this inventory for the closest match may take quite a long time.
	www.dontveter.com /basisofai/ch3.html (1492 words)

	HIW: Pattern Recognition: computers and human communications 030905
		The first task of pattern recognition software is finding patterns in streams of raw data like digital audio or video.
		Four types of pattern recognition software are key to computer interfaces: artificial neural networks, the hidden Markov model, nearest neighbor and support vector machines.
		The software recognizes patterns by comparing the ways a feature map resembles the maps of examples it was trained on.
	www.trnmag.com /Stories/2005/030905/HIW_Pattern_Recognition_030905.html (537 words)

	Nearest Neighbor Retrieval and Classification (Site not responding. Last check: 2007-11-02)
		Given a query image, the system finds the nearest neighbor of the query in the database, and outputs that the hand shape (or digit) in the query image is the hand shape (or digit) of the nearest neighbor.
		Saying that a database object is the "nearest neighbor" of the query implies that we have a way to measure distances between the query and database objects.
		An interesting experimental result of cascaded nearest neighbor classification is that classification time actually decreases as the size of the database increases, a behavior that is in stark contrast with the behavior of typical nearest neighbor classifiers.
	cs-people.bu.edu /athitsos/nearest-neighbors (1183 words)

	[No title] (Site not responding. Last check: 2007-11-02)
		NEAREST NEIGHBOR IN ONE DIMENSION Sorting the elements on a straight line or using a Binary Search Tree does the trick.
		NEAREST NEIGHBOR IN 2 DIMENSIONS In order to find the Nearest Neighbor when the point set is two-dimensional, we need to use tools from computational geometry, such as Voronoi Diagrams, which divide the two-dimensional point space into point regions.
		HIGHER DIMENSIONS The idea in higher dimensions is to reduce the Nearest Neighbor problem to a point location in the Voronoi diagram of d dimensions.
	theory.stanford.edu /~nmishra/CS361-2002/lecture12-scribe.doc (913 words)

	Excluded Middle Vantage Point Forests for Nearest Neighbor Search
		Nearest neighbor search is an important task for non-parametric density estimation, pattern recognition, information retrieval, memory-based reasoning, and vector quantization.
		This problem is related to but distinct from nearest neighbor search since a neighbor can be nearby even if a single coordinate is distant.
		nearest neighbor problem may be viewed as an instance of range search.
	www.pnylab.com /pny/papers/vp2/vp2/vp2.html (4704 words)

	The Pattern Recognition Basis of AI
		and feed it into the pattern recognition algorithm of your choice, the nearest neighbor algorithm is easy or backprop is very popular.
		The vertical and horizontal patterns are found in region 1 of the letter E pattern and there are no diagonals present, code this as the sequence (1,1,0,0).
		To get effective pattern recognition without this technique you would have to list large numbers of vectors, one for each possible way of drawing the E within the 21 x 21 area.
	www.dontveter.com /basisofai/char.html (1036 words)

	Piotr Indyk's thesis (Site not responding. Last check: 2007-11-02)
		The nearest neighbor problem is an example of a large class of proximity problems.
		Efficient reductions to dynamic approximate nearest neighbor from a variety of problems, including nearest neighbor, furthest neighbor, closest pair, minimum spanning tree, facility location and bottleneck matching.
		In particular, the sublinear-time algorithms for the near neighbor described above, together with the reductions, yield sublinear-time algorithms for nearest and furthest neighbor, as well as subquadratic-time algorithms for the remaining problems.
	theory.lcs.mit.edu /~indyk/thesis.html (594 words)

	ClopiNet Pattern Recognition (Site not responding. Last check: 2007-11-02)
		Pattern recognition is a special branch of machine learning in which we have considerable experience.
		Depending on the nature and quantity of data available we will advise you to use one or the other method or recognition, including "nearest neighbor" classification, radial basis functions, neural networks and hidden Markov models.
		Whether your data is clean or not and whether you need a solution quickly will impact the choice of the training algorithm that adjusts the parameter of the system.
	www.clopinet.com /clopinet/oldstuff/pr.html (237 words)

	A Statistical Learning/Pattern Recognition Glossary
		Pattern Recognition Information including books, a list of review papers, and bibliographic search.
		In other words, the variables are ordered, and each variable "depends" only on its neighbors in the sense of being conditionally independent of the others.
		The Markov property asserts conditional independence: given its immediate neighbors in the graph, a variable is independent of all other variables.
	www.cs.wisc.edu /~hzhang/glossary.html (4913 words)

	Winner-Update Algorithm for Nearest Neighbor Search
		This paper presents an algorithm, called the winner-update algorithm, for accelerating the nearest neighbor search.
		Our experiments have shown that the proposed algorithm can save a large amount of computation, especially when the distance between the query point and its nearest neighbor is relatively small.
		With slight modification, the winner-update algorithm can also speed up the search for k nearest neighbors, neighbors within a specified distance threshold, and neighbors close to the nearest neighbor.
	csdl2.computer.org /persagen/DLAbsToc.jsp?resourcePath=/dl/proceedings/&toc=comp/proceedings/icpr/2000/0750/02/0750toc.xml&DOI=10.1109/ICPR.2000.906172 (225 words)

	Home page for CS 295: Pattern Recognition
		This is the home page for the course CS 295: Pattern Recognition, offered by the Department of Computer Science at the University of Vermont, Spring 2002.
		Following a rigorous description of the statistical foundation of pattern classification, this course will survey a variety of statistical paradigms and popular pattern recognition algorithms.
		Luc Devroye, Lazlo Gyorfi, and Gabor Lugosi, A Probabilistic Theory of Pattern Recognition, Springer-Verlag, New York, 1996.
	www.emba.uvm.edu /~snapp/teaching/CS295PR/cs295pr.html (339 words)

	Document Information (Site not responding. Last check: 2007-11-02)
		Abstract: Nearest neighbor classifiers are a popular method for multiclass recognition in a wide range of computer vision and pattern recognition domains.
		At the same time, the accuracy of nearest neighbor classifiers is sensitive to the choice of distance measure.
		Our algorithm did achieve lower error rates in some of the datasets, which indicates that it is a method worth considering for nearest neighbor recognition in various pattern recognition domains.
	www.cs.bu.edu /groups/ivc/db/html/paper_view.php?id=138 (281 words)

	Lecture8 (Site not responding. Last check: 2007-11-02)
		Chapters 5, 6, 7, 11, and 26 of Devroye, Gyorfi, and Lugosi, "A Probabilistic Theory of Pattern Recognition" (on the class reading list) are devoted to the problem at hand and to its variations.
		Domeniconi, D. Gunopulos, "Efficient Local Flexible Nearest Neighbor Classification", to appear in the Proceedings of the Second SIAM Intl.
		Domeniconi, J. Peng, D. Gunopulos, "Adaptive Metric Nearest Neighbor Classification", in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, June 13-15, 2000, Hilton Head Island, South Carolina.
	www.ee.columbia.edu /~vittorio/Lecture8.html (468 words)

	Pattern Recognition (Site not responding. Last check: 2007-11-02)
		This course is an introduction to the subject of pattern recognition.
		We will cover theoretical foundations of classification and pattern recognition and discuss applications in character, speech and face recognition, and some applications in automation and robotics.
		For the project the undergraduate students will design and test a recognition system for the application of their choice subject to acceptance by the instructor.
	www.cs.concordia.ca /~comp473_2/fall2005 (282 words)

	Publications (Site not responding. Last check: 2007-11-02)
		Chapter in Frontiers of Pattern Recognition, Academic Press, New York, 1972.
		Proceedings of the First Int'l Joint Conference on Pattern Recognition, Washington, D.C., October 1973.
		The Relative Value of Labeled and Unlabeled Samples in Pattern Recognition with and Unknown Mixing Parameter.
	itg.stanford.edu /Publications/publications.html (1160 words)

	SNS Curriculum Vitae
		Pattern Recognition and Artificial Intelligence, 8(5), 1994, 1031-1052.
		Favata, G. Srikantan and S.N. Srihari, Handprinted Character/Digit Recognition using a Multiple Feature/Resolution Philosophy, Internation Workshop on the Frontiers of Handwriting Recognition, IWFHR IV, December 1994, Taipei, Taiwan.
		D.S. Lee, S.N. Srihari, and R. Gaborski, Bayes and neural network pattern recognition: a theoretical connection and empirical results with handwritten digits, Artificial Neural Networks and Statistical Pattern Recognition, I.S. Sethi and and A.K. Jain, editors, North-Holland, 1991, 89-108..
	www.cedar.buffalo.edu /~srihari/vitae.html (7232 words)

	[No title]
		Pattern Recognition, Volume 39, Issue 2, February 2006, Pages 164-17.
		Discriminant Projections Embedding for Nearest Neighbor Classification, in Progress in Pattern recognition, Image Analysis and Applications, LNCS 3287, Springer, pp.
		Adaboost to Classify Plaque Appearance in IVUS Images, in Progress in Pattern recognition, Image Analysis and Applications, LNCS 3287, Springer, pp.
	www.cvc.uab.es /~jordi/publications.htm (430 words)

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