English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Zendy Plus

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Zendy Tools

Zendy Open

The MNIST dataset of the handwritten digits is known as one of the commonlyused datasets for machine learning and computer vision research. We aim tostudy a widely applicable classification problem and apply a simple yetefficient K-nearest neighbor classifier with an enhanced heuristic. We evaluatethe performance of the K-nearest neighbor classification algorithm on the MNISTdataset where the $L2$ Euclidean distance metric is compared to a modifieddistance metric which utilizes the sliding window technique in order to avoidperformance degradation due to slight spatial misalignments. The accuracymetric and confusion matrices are used as the performance indicators to comparethe performance of the baseline algorithm versus the enhanced sliding windowmethod and results show significant improvement using this proposed method.

MNIST Dataset Classification Utilizing k-NN Classifier with Modified Sliding-window Metric