نتایج جستجو برای: طبقهبند k نزدیکترین همسایه knn
تعداد نتایج: 381296 فیلتر نتایج به سال:
Accurate probability-based ranking of instances is crucial in many real-world data mining applications. KNN (k-nearest neighbor) [1] has been intensively studied as an effective classification model in decades. However, its performance in ranking is unknown. In this paper, we conduct a systematic study on the ranking performance of KNN. At first, we compare KNN and KNNDW (KNN with distance weig...
The k-Nearest-Neighbours (kNN) is a simple but effective method for classification. The major drawbacks with respect to kNN are (1) its low efficiency being a lazy learning method prohibits it in many applications such as dynamic web mining for a large repository, and (2) its dependency on the selection of a “good value” for k. In this paper, we propose a novel kNN type method for classificatio...
Continuous K nearest neighbor queries (C-KNN) on moving objects retrieve the K nearest neighbors of all points along a query trajectory. In existing methods, the cost of retrieving the exact C-KNN data set is expensive, particularly in highly dynamic spatio-temporal applications. The cost includes the location updates of the moving objects when the velocities change over time and the number of ...
The paper contains the comparison between several class prediction methods (the K-Nearest Neighbour (KNN) algorithms and some variations of it) for classification of tumours using gene expression data. The KNN is a traditional classifier that uses a set of attributes for class prediction. Also are considered, the cases when these attributes (for KNN algorithm) are un-weighted (i.e. they all hav...
This paper proposes SV-kNNC, a new algorithm for k-Nearest Neighbor (kNN). This algorithm consists of three steps. First, Support Vector Machines (SVMs) are applied to select some important training data. Then, k-mean clustering is used to assign the weight to each training instance. Finally, unseen examples are classified by kNN. Fourteen datasets from the UCI repository were used to evaluate ...
Knn odel graphs of even order n and degree 1 blog 2 (n)c, W;n, are graphs which have been introduced some 25 years ago as the topology underlying a time optimal algorithm for gossiping among n nodes Knn o75]. However, they have been formally deened only 5 years ago FP94]. Since then, they have been widely studied as interconnection networks, mainly because of their good properties in terms of b...
چکیده مقدمه: تشخیص زودهنگام سرطان پستان نقش بسیار کلیدی در درمان و حیات بیمار ایفا می کند. امروزه با استفاده از خصوصیات استخراج شده از آزمایش آسپیراسیون سوزنی و الگوریتم های داده کاوی می توان روش های نوین و هوشمندی در نظام سلامت و درمان ارایه داد که با دقت بالایی قادر به تشخیص سرطان پستان باشند، هدف از انجام این مطالعه تشخیص سرطان پستان با استفاده از کاهش دو مرحله ای ویژگی های استخراج شده آسپیر...
Data classification attempts to assign a category or a class label to an unknown data object based on an available similar data set with class labels already assigned. K nearest neighbor (KNN) is a widely used classification technique in data mining. KNN assigns the majority class label of its closest neighbours to an unknown object, when classifying an unknown object. The computational efficie...
بیان احساس در ارتباطات روزمره از جایگاه ویژه ای برخوردار است. از جمله بسترهای نمود احساس، گفتار است. از این رو، یکی از جنبه های مهم در طبیعی سازی ارتباط میان انسان و ماشین، تشخیص حس گفتار و تولید بازخورد متناسب با احساس درک شده است. باوجود پیشرفت های گسترده در حوزة پردازش گفتار، استخراج و درک احساس پنهان در گفتار انسان، همچون خشم، شادی و جز این ها، از یک سو و تولید گفتار احساسی مناسب از سوی دیگ...
k-Nearest Neighbor (KNN) is one of the most popular algorithms for pattern recognition. Many researchers have found that the KNN classifier may decrease the precision of classification because of the uneven density of t raining samples .In view of the defect, an improved k-nearest neighbor algorithm is presented using shared nearest neighbor similarity which can compute similarity between test ...
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