A Neural Network with a Learning Vector Quantization Algorithm for Multiclass Classification Using a Modular Approach

Amezcua, Jonathan; Melín, Patricia; Castillo, Oscar

doi:10.1007/978-3-319-32229-2_13

Cited by 3 publications

(2 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Schwenker et al [6] describe this concept very clearly in their detailed analysis of the topic. This approach to supervised learning is heavily applied to neural networks [7], [8]. Neural networks thus find far and wide reaching applications that can have major impacts on society like the traffic camera system in the reference [9].…”

Section: B Pattern Matching and Clustering Neural Network Using Supmentioning

confidence: 99%

Pixel normalization from numeric data as input to neural networks: For machine learning and image processing

Sane

Agrawal

2017

2017 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET)

View full text Add to dashboard Cite

show abstract

Section: B Pattern Matching and Clustering Neural Network Using Supmentioning

confidence: 99%

Pixel normalization from numeric data as input to neural networks: For machine learning and image processing

Sane

Agrawal

2017

2017 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET)

View full text Add to dashboard Cite

show abstract

“…Metode ini melatih data secara kompetitif pada lapisan kompetitif yang secara otomatis mengklasifikasikan data masukan ke dalam salah satu kelas. LVQ tidak hanya melatih data secara terawasi (supervised learning), metode ini juga dapat melakukan clustering atau unsupervised learning untuk preprocessing pada dataset [12].…”

Section: ) Learning Vector Quantization (Lvq)unclassified

Pengenalan Kepribadian Seseorang Berdasarkan Pola Tulisan Tangan Menggunakan Jaringan Saraf Tiruan

Fadhilla¹,

Saf²,

Sahid³

2017

Jurnal Nasional Teknik Elektro dan Teknologi Informasi (JNTETI)

View full text Add to dashboard Cite

Abstract-Graphology is a study of representing personality based on handwriting. Individual's handwriting is unique and has own feature that it can be analyzed to understand personality. Graphology is used in some fields such as staffing, determining interest and talent. Some researches in graphology using artificial intelligence have been studied before. However, most of the researches still used one handwriting feature and did not classify into personality type. In this study, using some features of handwriting, i.e. left margin, right margin, size, and slant to classify personality type. Personality is classified based on Myers-Briggs Type Indicator (MBTI) using Back Propagation and Learning Vector Quantization method. The result shows that Learning Vector Quantization has better performance, with 90% accuracy, than Back Propagation, which has 82% accuracy.Intisari-Grafologi adalah ilmu yang merepresentasikan kepribadian seseorang berdasarkan tulisan tangan. Tulisan tangan setiap orang bersifat unik dan memiliki karakteristiknya masing-masing, sehingga dapat dianalisis untuk memahami watak seseorang. Grafologi digunakan dalam berbagai bidang, seperti penentuan minat bakat dan penempatan karyawan. Penelitian yang menerapkan kecerdasan buatan di bidang grafologi sudah dilakukan oleh beberapa peneliti. Akan tetapi, sebagian besar masih menggunakan satu karakteristik tulisan tangan dan tidak mengklasifikasikan ke dalam salah satu tipe kepribadian. Oleh karena itu, pada makalah ini dibangun aplikasi pengenalan kepribadian berdasarkan beberapa karakterisitik tulisan tangan yaitu margin kiri, margin kanan, ukuran, kemiringan, dan bentuk huruf. Kepribadian diklasifikasikan berdasarkan Myers-Briggs Type Indicator (MBTI) menggunakan metode Back Propagation dan Learning Vector Quantitazion. Berdasarkan hasil pengujian, diketahui metode Learning Vector Quantitazion, dengan tingkat akurasi 90%, memiliki kinerja yang lebih baik dibandingkan Back Propagation yang memiliki tingkat akurasi sebesar 82%.

show abstract

Extraction and Classification of Human Body Parameters for Gait Analysis

Souza

Stemmer

2018

J Control Autom Electr Syst

View full text Add to dashboard Cite

A Neural Network with a Learning Vector Quantization Algorithm for Multiclass Classification Using a Modular Approach

Cited by 3 publications

References 11 publications

Pixel normalization from numeric data as input to neural networks: For machine learning and image processing

Pixel normalization from numeric data as input to neural networks: For machine learning and image processing

Pengenalan Kepribadian Seseorang Berdasarkan Pola Tulisan Tangan Menggunakan Jaringan Saraf Tiruan

Extraction and Classification of Human Body Parameters for Gait Analysis

Contact Info

Product

Resources

About