An Implicit Memory-Based Method for Supervised Pattern Recognition

Abstract

How the human brain does recognition is still an open question. No physical or biological experiment can fully reveal this process. Psychological evidence is more about describing phenomena and laws than explaining the physiological processes behind them. The need for interpretability is well recognized. This paper proposes a new method for supervised pattern recognition based on the working pattern of implicit memory. The artificial neural network (ANN) is trained to simulate implicit memory. When an input vector is not in the training set, the ANN can treat the input as a “do not care” term. The ANN may output any value when the input is a “do not care” term since the training process needs to use as few neurons as possible. The trained ANN can be expressed as a function to design a pattern recognition algorithm. Using the Mixed National Institute of Standards and Technology database, the experiments show the efficiency of the pattern recognition method.

Segmentasi Citra Menggunakan Metode Otsu dalam Pengenalan Pola Sederhana

“…Pengenalan pola bertujuan untuk mengektrak informasi menggunakan pengolahan citra [3]. Pengenalan pola terdiri dari 2 (dua) kategori, yaitu ektraksi fitur dan klasifikasi pola yang digunakan untuk mengidentifikasi kauntitas, karakteristik, tekstur pada pola [4]. Citra digital atau disebut raster image merupakan simbolisasi numerik pada konfigurasi nilai biner 8 bit dari citra dua dimensi yang disebut dengan piksel dengan jumlah baris dan kolom yang tetap [5], sedangkan pengolahan citra merupakan suatu cara untuk ektraksi dan mengenal suatu objek dari suatu gambar [6].…”