Insights Into Efficient k-Nearest Neighbor Classification With Convolutional Neural Codes
نویسندگان
چکیده
منابع مشابه
Clustering-based k-nearest neighbor classification for large-scale data with neural codes representation
While standing as one of the most widely considered and successful supervised classification algorithms, the k-Nearest Neighbor (kNN) classifier generally depicts a poor efficiency due to being an instance-based method. In this sense, Approximated Similarity Search (ASS) stands as a possible alternative to improve those efficiency issues at the expense of typically lowering the performance of t...
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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...
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متن کاملAn Improved K-Nearest Neighbor with Crow Search Algorithm for Feature Selection in Text Documents Classification
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...
متن کاملProblem Set 1 K-nearest Neighbor Classification
In this part, you will implement k-Nearest Neighbor (k-NN) algorithm on the 8scenes category dataset of Oliva and Torralba [1]. You are given a total of 800 labeled training images (containing 100 images for each class) and 1888 unlabeled testing images. Figure 1 shows some sample images from the data set. Your task is to analyze the performance of k-NN algorithm in classifying photographs into...
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ژورنال
عنوان ژورنال: IEEE Access
سال: 2020
ISSN: 2169-3536
DOI: 10.1109/access.2020.2997387