KNN classification is an improvisational learning mode, in which they are carried out only
when a test data is predicted that set a suitable K value and search the K nearest neighbors …

Abstract Convolutional Neural Network (CNN) or Recurrent Neural Network (RNN) based
text classification algorithms currently in use can successfully extract local textual features …

Abstract kNN adalah salah satu metode yang popular karena mudah dieksploitasi,
generalisasi yang biak, mudah dimengerti, kemampuan beradaptasi ke ruang fitur yang …

The drift of metal oxide semiconductor (MOX) chemical sensors is one of the most important
topics in this field. The work aims to test the performance of MOX gas sensors over the aging …

One-class CNNs have shown promise in novelty detection. However, very less work has
been done on extending them to multiclass classification. The proposed approach is a …

Learning effective feature spaces for KNN (K-Nearest Neighbor) classifiers is critical for their
performance. Existing KNN loss functions designed to optimize CNNs in R n feature spaces …

We present a simple yet efficient Hybrid Classifier based on Deep Learning and
Reinforcement Learning. Q-Learning is used with two Q-states and four actions …