نتایج جستجو برای: knn algorithm

تعداد نتایج: 756003  

2008
ZALÁN BODÓ ZSOLT MINIER

The k-nearest neighbor (kNN) is one of the simplest classification methods used in machine learning. Since the main component of kNN is a distance metric, kernelization of kNN is possible. In this paper kNN and semi-supervised kNN algorithms are empirically compared on two data sets (the USPS data set and a subset of the Reuters-21578 text categorization corpus). We use a soft version of the kN...

Journal: :جنگل و فرآورده های چوب 0
رویا عابدی دانش آموخته دکترای جنگلداری/دانشکده منابع طبیعی، دانشگاه گیلان سید امیراسلام بنیاد استاد/دانشکده منابع طبیعی دانشگاه گیلان اسدالله شاه بهرامی دانشیار/دانشگاه گیلان

proper forest management needs quantitative and precise estimates of forest stands characteristics. remotely sensed imageries, due to accurate and broad spatial information, has become a cost-effective tool in forest management. classification of forest attributes and generation of thematic maps are among the common applications of remote sensing. the objective of this study was to optimize the...

2010
Tao Yang Longbing Cao Chengqi Zhang

In this paper, a novel prototype reduction algorithm is proposed, which aims at reducing the storage requirement and enhancing the online speed while retaining the same level of accuracy for a K-nearest neighbor (KNN) classifier. To achieve this goal, our proposed algorithm learns the weighted similarity function for a KNN classifier by maximizing the leave-one-out cross-validation accuracy. Un...

Journal: :Inf. Sci. 2016
Enmei Tu Yaqian Zhang Lin Zhu Jie Yang Nikola K. Kasabov

k Nearest Neighbors (kNN) is one of the most widely used supervised learning algorithms to classify Gaussian distributed data, but it does not achieve good results when it is applied to nonlinear manifold distributed data, especially when a very limited amount of labeled samples are available. In this paper, we propose a new graph-based kNN algorithm which can effectively handle both Gaussian d...

Introduction: Breast cancer is the second cause of mortality among women. Early detection is the only rescue to reduce the risk of breast cancer mortality. Traditional methods cannot effectively diagnose tumor since they are based on the assumption of well-balanced dataset.. However, a hybrid method can help to alleviate the two-class imbalance problem existing in the ...

2016
Oreoluwa Alebiosu

A data set in machine learning is simply a table representing collections of entries. Each entry, has attributes of which can be thought as the columns in a table. A label describes the class or category of an entry. For binary labels each entry may posses a class label of 0 or 1 of which can be thought of as negative or positive labels. Model and classifier are used synonymously. Two phases ar...

The Internet provides easy access to a kind of library resources. However, classification of documents from a large amount of data is still an issue and demands time and energy to find certain documents. Classification of similar documents in specific classes of data can reduce the time for searching the required data, particularly text documents. This is further facilitated by using Artificial...

1993
Dietrich Wettschereck Thomas G. Dietterich

Four versions of a k-nearest neighbor algorithm with locally adaptive k are introduced and compared to the basic k-nearest neighbor algorithm (kNN). Locally adaptive kNN algorithms choose the value of k that should be used to classify a query by consulting the results of cross-validation computations in the local neighborhood of the query. Local kNN methods are shown to perform similar to kNN i...

2015
Alexander Fargus

Condition monitoring systems for prognostics and diagnostics can enable large and complex systems to be operated more safely, at a lower cost and have a longer lifetime than is possible without them. AURA Alert is a condition monitoring system that uses a fast approximate k Nearest Neighbour (kNN) search of a timeseries database containing known system states to identify anomalous system behavi...

2016
Gang Mei Nengxiong Xu Liangliang Xu

This paper presents an efficient parallel Adaptive Inverse Distance Weighting (AIDW) interpolation algorithm on modern Graphics Processing Unit (GPU). The presented algorithm is an improvement of our previous GPU-accelerated AIDW algorithm by adopting fast k-nearest neighbors (kNN) search. In AIDW, it needs to find several nearest neighboring data points for each interpolated point to adaptivel...

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