scholarly journals Aspects in Classification Learning - Review of Recent Developments in Learning Vector Quantization

2014 ◽  
Vol 39 (2) ◽  
pp. 79-105 ◽  
Author(s):  
M. Kaden ◽  
M. Lange ◽  
D. Nebel ◽  
M. Riedel ◽  
T. Geweniger ◽  
...  
Keyword(s):  
Machine Learning ◽  
Vector Quantization ◽  
Incomplete Data ◽  
Learning Vector Quantization ◽  
Classification Models ◽  
Classification Learning ◽  
Recent Developments ◽  
Optimal Classification ◽  
Classification Tasks ◽  
Basic Learning

Abstract .Classification is one of the most frequent tasks in machine learning. However, the variety of classification tasks as well as classifier methods is huge. Thus the question is coming up: which classifier is suitable for a given problem or how can we utilize a certain classifier model for different tasks in classification learning. This paper focuses on learning vector quantization classifiers as one of the most intuitive prototype based classification models. Recent extensions and modifications of the basic learning vector quantization algorithm, which are proposed in the last years, are highlighted and also discussed in relation to particular classification task scenarios like imbalanced and/or incomplete data, prior data knowledge, classification guarantees or adaptive data metrics for optimal classification.

Classification with Incomplete Data

2010 ◽  
pp. 147-175 ◽  
Author(s):  
Pedro J. García-Laencina ◽  
Juan Morales-Sánchez ◽  
Rafael Verdú-Monedero ◽  
Jorge Larrey-Ruiz ◽  
José-Luis Sancho-Gómez ◽  
...  
Keyword(s):  
Machine Learning ◽  
Missing Data ◽  
Pattern Classification ◽  
Incomplete Data ◽  
Data Handling ◽  
Advantages And Disadvantages ◽  
Word Classification ◽  
Classification Tasks ◽  
Missing Data Techniques ◽  
Fundamental Requirement

Many real-word classification scenarios suffer a common drawback: missing, or incomplete, data. The ability of missing data handling has become a fundamental requirement for pattern classification because the absence of certain values for relevant data attributes can seriously affect the accuracy of classification results. This chapter focuses on incomplete pattern classification. The research works on this topic currently grows wider and it is well known how useful and efficient are most of the solutions based on machine learning. This chapter analyzes the most popular and proper missing data techniques based on machine learning for solving pattern classification tasks, trying to highlight their advantages and disadvantages.

scholarly journals Identifikasi Jenis Kayu Berdasarkan Fitur Tekstur Local Binary Pattern Menggunakan Metode Learning Vector Quantization

2021 ◽  
Vol 10 (3) ◽  
pp. 157
Author(s):  
Ni Made Yeni Dwi Rahayu ◽  
Made Windu Antara Kesiman ◽  
I Gede Aris Gunadi
Keyword(s):  
Machine Learning ◽  
Vector Quantization ◽  
Local Binary Pattern ◽  
Learning Vector Quantization ◽  
Data Input

Pada umumnya pengenalan jenis kayu masih dilakukan dengan menggunakan indera penglihatan dan penciuman. Hal tersebut dapat mempengaruhi proses jual beli dimana waktu yang dibutuhkan untuk pengenalan kayu menjadi lebih lama sehingga menyebabkan proses bisnis menjadi kurang efektif. Penelitian ini bertujuan untuk membangun suatu model machine learning untuk proses identifikasi jenis kayu berdasarkan fitur teksur citra pada kayu. Metode Local Binary Pattern (LBP) digunakan dalam proses ekstraksi ciri untuk menghasilkan vektor ciri yang dijadikan data input pada proses klasifikasi citra dengan menggunakan metode Learning Vector Quantization (LVQ). Parameter yang digunakan pada metode LBP meliputi numpoint dan radius dengan nilai 1 sampai 10. Hasil penelitian dari metode ini didapatkan akurasi tertinggi 68,33% pada numpoint 2 dan radius 1. Hasil pengujian yang cukup rendah dapat dipengaruhi oleh beberapa faktor yaitu jumlah citra latih dan terdapat beberapa citra kayu memiliki pola yang hampir sama.

scholarly journals A Synthetic Player for Ayὸ Board Game Using Alpha-Beta Search and Learning Vector Quantization

2016 ◽  
Vol 9 (3) ◽  
pp. 1
Author(s):  
Oluwatobi, A. Ayilara ◽  
Anuoluwapo, O. Ajayi ◽  
Kudirat, O. Jimoh
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Vector Quantization ◽  
Search Algorithm ◽  
Learning Vector Quantization ◽  
Game Playing ◽  
Learning Approaches ◽  
Board Game ◽  
Significant Interest ◽  
Alpha Beta

Game playing especially, Ayὸ game has been an important topic of research in artificial intelligence and several machine learning approaches have been used, but the need to optimize computing resources is important to encourage significant interest of users. This study presents a synthetic player (Ayὸ) implemented using Alpha-beta search and Learning Vector Quantization network. The program for the board game was written in Java and MATLAB. Evaluation of the synthetic player was carried out in terms of the win percentage and game length. The synthetic player had a better efficiency compared to the traditional Alpha-beta search algorithm.

scholarly journals Supervised Learning Using Homology Stable Rank Kernels

2021 ◽  
Vol 7 ◽  
Author(s):  
Jens Agerberg ◽  
Ryan Ramanujam ◽  
Martina Scolamiero ◽  
Wojciech Chachólski
Keyword(s):  
Machine Learning ◽  
Data Analysis ◽  
Real World ◽  
Fundamental Property ◽  
Stable Rank ◽  
Topological Data Analysis ◽  
Improve Accuracy ◽  
Recent Developments ◽  
Classification Tasks ◽  
Real World Datasets

Exciting recent developments in Topological Data Analysis have aimed at combining homology-based invariants with Machine Learning. In this article, we use hierarchical stabilization to bridge between persistence and kernel-based methods by introducing the so-called stable rank kernels. A fundamental property of the stable rank kernels is that they depend on metrics to compare persistence modules. We illustrate their use on artificial and real-world datasets and show that by varying the metric we can improve accuracy in classification tasks.

Blocked 3×2 Cross-Validated t-Test for Comparing Supervised Classification Learning Algorithms

2014 ◽  
Vol 26 (1) ◽  
pp. 208-235 ◽  
Author(s):  
Wang Yu ◽  
Wang Ruibo ◽  
Jia Huichen ◽  
Li Jihong
Keyword(s):  
Machine Learning ◽  
Supervised Classification ◽  
Cross Validation ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Variance Estimator ◽  
Generalization Error ◽  
Classification Learning ◽  
Test Of Significance ◽  
Classification Tasks

In the research of machine learning algorithms for classification tasks, the comparison of the performances of algorithms is extremely important, and a statistical test of significance for generalization error is often used to perform it in the machine learning literature. In view of the randomness of partitions in cross-validation, a new blocked 3×2 cross-validation is proposed to estimate generalization error in this letter. We then conduct an analysis of variance of the blocked 3×2 cross-validated estimator. A relatively conservative variance estimator that considers the correlation between any two two-fold cross-validations, and was previously neglected in 5×2 cross-validated t and F-tests is put forward. A corresponding test using this variance estimator is presented to compare the performances of algorithms. Simulated results show that the performance of our test is comparable with that of 5×2 cross-validated tests but with less computation complexity.

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