k-NN method
Short-Term Traffic Prediction Using a Binary Neural Network.
... The k-NN approach satisfies all the requirements for an online predictor for ...IDS. K-NN can naturally incorporate spatially and temporally lagged ...The method does not require ...
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Comparison of Naive Basian and K NN Classifier
... the K-Nearest Neighbor [10], which was maturely developed after some time. K-Nearest Neighbor can be calculated by calculating Euclidian distance, although other measures are available, through Euclidian ...
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An Enhanced Approach for Treating Missing Value using Boosted K NN
... deletion method produces better results than the replacing missing value using mean but removing the missing attribute value may lead to the misclassification and wrong ...deletion method it uses only 232 ...
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Research of liver cancer detection based on improved K NN algorithm
... To remove features with little effect on classification, we introduced the PCA into the process of classification of K-NN algorithm. The goal of PCA is to identify the most meaningful basis to re-express a ...
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An improved k NN algorithm for localization in multipath environments
... (improved k-NN) is almost the same as the re- sult of Strength (improved k-NN), but our algorithm is much better under higher SNR ...in k-NN ...
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K-NN CLASSIFIER FOR PREDICTION OF SWINE INFLUENZA VIRUS
... The above Table No.II shows result of Euclidean distance of all state from the Rajasthan so here we can clearly see that nearest near neighbors means minimum distance of state from Rajasthan we can there are three values ...
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Analysis of Market Trajectory Data using k-NN
... a method of estimating a group of spectators based on visitor information using k-Nearest Neighborhood (k-NN) ...The k-NN is an algorithm that classifies the input data into ...
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A high performance k-NN approach using binary neural networks
... The first evaluation data set is synthetic (REAL) and is generated using a Java random number generator application. It contains 200,000 non-multivariate vectors with 14 real-valued attributes. All attributes range ...
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Prediction Using Back Propagation and k-Nearest Neighbour (k-NN) Algorithm
... Prediction of stock price using back propagation and k-NN algorithm is based on real time market prediction. Robust model has constructed, so prediction price is close to actual price. Stock price predicted ...
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An Efficient & Detailed Digital Image Feature Extraction Method Using K-NN Algorithm and Image Tags
... using k-Nearest Neighbors ...a method that prepares a database of features extracted from images provided to the system as an input from the local ...to k- ...
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GRAPHICAL REPRESENTATION OF TEXTUAL DATA USING TEXT CATEGORIZATION SYSTEM
... The K-NN algorithm is simple and with high precision for classifying the ...based K-NN forms the base for the project as its classification speed is accurate and also the K-NN is ...
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Devanagari Handwritten Character Recognition Using Neural Network
... algorithms, K-Nearest Neighbour (KNN) and Support Vector Machine (SVM) to classify the numeric ...kernel, k-Nearest Neighbor (k-NN) with different values of k and multi-layer perceptron ...
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A Comparative Study of Fuzzy K-mean & Fuzzy K-NN & Fuzzy back propagation
... Fuzzy K-Nearest Neighbor algorithms [14] have two main advantages over the traditional (crisp) K-Nearest Neighbor algorithms [15][16]. Firstly, while determining the class of the current residue, the ...
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Efficient Image Retrieval via Decoupling Diffusion into Online and Offline Processing
... our method with other competitive meth- ods that use global and regional ...apply k-NN search using the global features to obtain the closest im- ages to that ...
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SURVEY ON INFORMATION EXTRACTION FROM CHEMICAL COMPOUND LITERATURES: TECHNIQUES AND CHALLENGES
... [4] J. Raza, J. P. Liyanage, H. A. Atat and J. Lee, “A comparative study of maintenance data classification based on neural networks, logistic regression and support vector machine”, Journal of Quality in Maintenance ...
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A survey of outlier detection methodologies
... window method can provide a hard boundary for outlier detection by classifying new exemplar as normal if they belong to a kernel and an outlier ...a k-means module with the MLP mod- ule in an aircraft ...
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Blame Based Noise Reduction: An Alternative Perspective on Noise Reduction for Lazy Learning
... a k-NN algorithm is improved, or at least not significantly damaged, by noise reduction preprocessing in all cases except for two ...baseline k-NN obtains the best accuracy on both the ...
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An intelligent weighted outlier detection method for intrusion detection using MST and k NN
... Intrusion Detection System (IDS) is a potential part in the area of network security system. An effective intrusion detection system is necessary for providing effective communications in the past world. The major ...
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Analyzing Entity Framework Technology for an Indoor Decoration Based Recommendation System
... In this study, two different technologies, which are ADO.Net and Entity Framework technologies, were tested on the Collaborative Filtering method which is a data mining method. Both technologies have ...
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Blur Classification and Deblurring of Images
... The efficiency issues for the classification the blur type is monitored by using an edge detector to obtain binary input values for the DBN training which benefits the blur analysis task. The general regression neural ...
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