K-nn
A DIFFERENTIAL DIAGNOSIS IN MEDICAL FIELD USING SOA AND K-NN CLASSIFIER TECHNIQUE
... including k-NN classifier [4] and the next probable diseases by performing differential diagnosis using the Hopfield neural network theory and Large Memory Storage and Retrieval (LAMSTAR) ...
5
Non-Asymptotic Uniform Rates of Consistency for k-NN Regression
... that k-NN regres- sion is able to automatically adapt to the intrinsic dimension under various risk measures: the rates depend only on the intrinsic dimension and independent of ambient ...
8
An Hybrid Intrusion Detection Approach based on SVM Classification and k NN
... Generally, inconsistency based intrusion detection can be considered as a characterization issue, one that decides net-work attacks by grouping system activity into ordinary and strange associations. Likewise, regulated ...
14
A Study on Content Based Image Retrieval System using K-NN Algorithm and Mobile Agents
... The value of property is known from these sets of objects the neighbours are taken for the correct classification or, in the case of regression, the value of. This can be thought of as the training set for the ...
9
Enhanced Cache Gird Partitioning Technique for K-NN Queries
... continuous k-NN query which continuously returns the k nearest objects to the current location of the ...continuous k-NN queries in mobile DBSs is ...
7
A Cuisine Based Recommender System Using k NN And Mapreduce Approach
... The restaurant recommender system based on cuisine search is developed based on kNN based MapReduce approach. This can be useful to individual users. The methodology proposed may be useful to any person who visits an ...
5
Ensemble Neural Network and K-NN Classifiers for Intrusion Detection
... a k-nn classifier for classification of five-class data which is (normal, dos, u2r, r2l, and ...technique. K-nn classified data which were misclassified by NN & then apply bagging ...
5
Analysis of Market Trajectory Data using k-NN
... using k-Nearest Neighborhood (k-NN) algorithm. The k-NN is an algorithm that classifies the input data into items belonging to k closest training data in a specific ...
6
K-NN CLASSIFIER FOR PREDICTION OF SWINE INFLUENZA VIRUS
... .The k-nearest neighbor algorithm is one of the simplest machine learning ...The K-nearest neighbor classification, the output is class membership of an object is classified by majority vote of its ...
6
Segmentation of Lung Nodule Image Using Global Thresholding and Classification by K-NN
... In k-NN classification the neighbours are chosen to which the objects property value is ...of k-NN algorithm is Feature ...using k-NN which includes feature extraction and ...
5
BIG DATA ANALYSIS USING SVM AND K-NN DATA MINING TECHNIQUES
... Data mining efficiently acts here by predicting useful information from big, past and noisy data. Prediction of aircraft accident is a critical factor. Thus it has been implemented with three efficient data mining ...
8
Pedagogy And Reduction Of K-nn Algorithm For Filtering Samples In The Breast Cancer Treatment
... As per analysis among different types of cancers, Breast cancer is widespread. On the other hand, if it diagnosed in early stage there is more chance of curing it. ML method was introduced for diagnosing the breast ...
6
An Efficient Mining Model For Enhancing Text Classification Using k NN
... If k = 3 it is assigned to the second class because there are 2 triangles and only 1 square inside the inner ...If k = 5 it is assigned to the first class (3 squares ...phase, k is a user-defined ...
9
PRIVACY PRESERVING TECHNIQUE USING K-NN CLASSIFICATION IN OUTSOURCES ENVIRONMENT
... secure k-Nearest Neighbour (k-NN) classifier over encrypted data in the ...secure k-NN classifier over encrypted data under the semi- honest ...
6
Performance Analysis of SVM, k-NN and BPNN Classifiers for Motor Imagery
... the k-NN is very simple algorithm to understand and implement, but it is slower when compared with ...SVM, k-NN is lazy algorithm , it means it start learning when it see the testing data ...
5
An Enhanced Approach for Treating Missing Value using Boosted K NN
... In the real world applications the information to be processed are raw data. The data used for information retrieval are not complete sets. Handling the missing value of the dataset is a serious problem for predicting ...
7
k-NN Embedding Stability for word2vec Hyper-Parametrisation in Scientific Text
... sionality, k-NN stability was adopted to find the optimal window size for the used scientific corpus in this ...Then k-NN stability was com- puted respectively based on the previous embedding ...
15
An improved k NN algorithm for localization in multipath environments
... is developed to improve the overall accuracy. As an ef- fective scene analysis localization algorithm, the k- nearest neighbor (k-NN) [22] algorithm is proposed by utilizing reference tags. This ...
10
Comparison of Naive Basian and K NN Classifier
... between k-nearest neighbor and naïve basin classifier based on the subset of ...that K-NN classifier gets competition with naïve basian ...performance K-NN classifier is far better then ...
6
Prediction Using Back Propagation and k-Nearest Neighbour (k-NN) Algorithm
... The genetic algorithm had been adopted by Shin (et al. 2005); the number of trading rules was generated for Korea Stock Price Index 200 (KOSPI 200), in Sweden Hellestrom and Homlstrom (1998) used a geometric scrutiny ...
5