K-nearest neighbour technique for the effective prediction of refrigeration parameter compatible for automobile
نویسندگان
چکیده
منابع مشابه
k-Nearest Neighbour Classifiers
Perhaps the most straightforward classifier in the arsenal or machine learning techniques is the Nearest Neighbour Classifier – classification is achieved by identifying the nearest neighbours to a query example and using those neighbours to determine the class of the query. This approach to classification is of particular importance today because issues of poor run-time performance is not such...
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The k Nearest Neighbour (kNN) method is a widely used technique which has found several applications in clustering and classification. In this paper, we focus on classification problems and we propose modifications of the nearest neighbour method that exploit information from the structure of a dataset. The results of our experiments using datasets from the UCI repository demonstrate that the c...
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Let P be a Poisson process of intensity one in a square Sn of area n. For a fixed integer k, join every point of P to its k nearest neighbours, creating an undirected random geometric graph Gn,k. We prove that there exists a critical constant ccrit such that for c < ccrit, Gn,⌊c logn⌋ is disconnected with probability tending to 1 as n → ∞, and for c > ccrit, Gn,⌊c logn⌋ is connected with probab...
متن کاملConvergence of random k-nearest-neighbour imputation
Random k-nearest-neighbour (RKNN) imputation is an established algorithm for filling in missing values in data sets. Assume that data are missing in a random way, so that missingness is independent of unobserved values (MAR), and assume there is a minimum positive probability of a response vector being complete. Then RKNN, with k equal to the square root of the sample size, asymptotically produ...
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LetP be a Poisson process of intensity one in a squareSn of arean. We construct a random geometric graph Gn,k by joining each point of P to its k ≡ k(n) nearest neighbours. Recently, Xue and Kumar proved that if k ≤ 0.074 log n then the probability that Gn,k is connected tends to 0 as n → ∞ while, if k ≥ 5.1774 log n, then the probability that Gn,k is connected tends to 1 as n → ∞. They conject...
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ژورنال
عنوان ژورنال: Thermal Science
سال: 2020
ISSN: 0354-9836,2334-7163
DOI: 10.2298/tsci190623436p