SavedAdd your journal

Journals / Anadolu Üniversitesi Bilim ve Teknoloji Dergisi :A-Uygulamalı Bilimler ve Mühendislik / 2005 / Cilt: 6 - Sayı: 2

Comparison of single layer neural network and linear discriminant function

Tek katmanlı yapay sinir ağları ve doğrusal diskriminant fonksiyonunun karşılaştırması

Güvenç ARSLAN
İlknur ÖZMEN
Pages
175–182
Publication Date
2022-10-11
DOI
—
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Both methods are then applied on the same data set in order to stress their strong and weak properties.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:In this study Fisher's linear discriminant function and winner-take-all groups, also known as linear machines, are studied.
📊
Key Findings & Data:First, some basic information on Fisher's linear discriminant function and on linear machines, a special type of single layer neural networks, is given.
💡
Conclusion & Impact:Both methods are then applied on the same data set in order to stress their strong and weak properties.
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:Domain Parameters

Abstract

In this study Fisher's linear discriminant function and winner-take-all groups, also known as linear machines, are studied. First, some basic information on Fisher's linear discriminant function and on linear machines, a special type of single layer neural networks, is given. Both methods are then applied on the same data set in order to stress their strong and weak properties.

Özet

Fisher doğrusal diskriminant fonksiyonu (Fisher's linear discriminant function) ve doğrusal makinelerin (winner-take-all groups / linear machines) incelendiği bu çalışmada, öncelikle Fisher doğrusal diskriminant fonksiyonu ve tek katmanlı yapay sinir ağlarının (single layer neural networks) özel bir durumu olan doğrusal makineler hakkında bazı temel bilgiler verilmiştir. Daha sonra her iki yöntem, aynı veri kümesine uygulanarak güçlü ve zayıf yönleri vurgulanmaya çalışılmıştır.

🔖

Cite This Article

ARSLAN, G., ÖZMEN, İ. (2022). Comparison of single layer neural network and linear discriminant function. Anadolu Üniversitesi Bilim ve Teknoloji Dergisi :A-Uygulamalı Bilimler ve Mühendislik, Cilt: 6 - Sayı: 2, 175–182. https://ajindex.com/articles/438426

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (6)

  1. Boser, B., Guyon, I. ve Vapnik, V.N. (1992). A training algorithm for optimal margin classifiers. Fifth Annual Workshop on Computational Learning Theory, ss. 144-152.
  2. Morgan Kaufmann, San Mateo. CA. Gallant, S. I. (1994). Neural Network Learning and Expert Systems. The MIT Press.
  3. Massachusetts. Haykin, S. (1999). Neural Networks: A Comprehensive Foundation. Prentice Hall, New Jersey. Hebb, D.O. (1949). The Organization of Behavior: A Neuropsychological Theory. Wiley, New York. Johnson, R. A. ve Wichern, D. W. (2002). Applied Multivariate Statistical Analyses. Prentice Hall, New Jersey. McCulloch, W.S. ve Pitts, W. (1943). A logical calculus of the ideas immanent in nervous activity. Bulletin of Mathematical Biophysics 5, 114-133.
  4. Özdamar, K. (1999). Paket Programlar İle İstatistiksel Veri Analizi II. Kaan Kitabevi,
  5. Eskişehir. Rosenblatt, F. (1958). The Perceptron: A probabilistic model for information storage and organization in the brain. Psychological Review 65, 386-408.
  6. Schalkoff, R. J. (1992). Pattern Recognition: Statistical, Structural and Neural Approaches. John Wiley & Sons. Tatlıdil, H. (1996). Uygulamalı Çok Değişkenli İstatistiksel Analiz. Cem Web Ofset Ltd. Şti, Ankara.