2015 Fourteenth Mexican International Conference on Artificial Intelligence (MICAI)最新文献_第2页

Automatic Summarization of News Articles in Mobile Devices 移动设备上新闻文章的自动摘要

2015 Fourteenth Mexican International Conference on Artificial Intelligence (MICAI) Pub Date : 2015-10-25 DOI: 10.1109/MICAI.2015.8

L. Cabral, Rinaldo Lima, R. Lins, Manoel Neto, Rafael Ferreira, S. Simske, M. Riss

引用次数: 7

An Expert System to Detect Risk Levels in Small and Medium Enterprises (SMEs) 侦测中小企业风险水平的专家系统

2015 Fourteenth Mexican International Conference on Artificial Intelligence (MICAI) Pub Date : 2015-10-25 DOI: 10.1109/MICAI.2015.39

Zaila Hernandez, Talhia H. Hernandez, Nestor Velasco Bermeo, Blanca Monroy

引用次数: 2

Multivariate Approximation Methods Using Polynomial Models: A Comparative Study 使用多项式模型的多元逼近方法:比较研究

2015 Fourteenth Mexican International Conference on Artificial Intelligence (MICAI) Pub Date : 2015-10-25 DOI: 10.1109/MICAI.2015.26

I. López-Peña, Ángel Fernando Kuri Morales

{"title":"Multivariate Approximation Methods Using Polynomial Models: A Comparative Study","authors":"I. López-Peña, Ángel Fernando Kuri Morales","doi":"10.1109/MICAI.2015.26","DOIUrl":"https://doi.org/10.1109/MICAI.2015.26","url":null,"abstract":"A frequent problem in artificial intelligence is the one associated with the so-called supervised learning: the need to find an expression of a dependent variable as a function of several independent ones. There are several algorithms that allow us to find a solution to the bivariate problems. However, the true challenge arises when the number of independent variables is large. Relatively new tools have been developed to tackle this kind of problems. Thus, multi-Layer Perceptron networks (MLPs) may be seen as multivariate approximation algorithms. However, a commonly cited disadvantage of MLPs is that they remain a \"black-box\" kind of method: they do not yield an explicit closed expression to the solution. Rather, we are left with the need of expressing it via the architecture of the MLP and the value of the trained connections. In this paper we explore three methods that allow us to express the solution to multivariate problems in a closed form: a) Fast Ascent (FA), b) Levenberg-Marquardt (LM) and c) Powell's Dog-Leg (PM) algorithms. These yield closed expressions when presented with multiple independent variable problems. In this paper we discuss and compare these four methods and their possible application to pattern recognition in mobile robot environments and artificial intelligence in general.","PeriodicalId":448255,"journal":{"name":"2015 Fourteenth Mexican International Conference on Artificial Intelligence (MICAI)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114784213","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Evolutionary Robot with the Task to Remain Standing 进化机器人的任务是保持站立

2015 Fourteenth Mexican International Conference on Artificial Intelligence (MICAI) Pub Date : 2015-10-25 DOI: 10.1109/MICAI.2015.31

Antonio Zalapa-Elias, A. Rodríguez-Liñán, L. Torres-Treviño

引用次数: 0

Design of an Optimal Heat Sink for Microelectronic Devices Using Entropy Generation Minimization 基于熵产最小化的微电子器件最佳散热器设计

2015 Fourteenth Mexican International Conference on Artificial Intelligence (MICAI) Pub Date : 2015-10-25 DOI: 10.1109/MICAI.2015.42

J. M. Cruz-Duarte, J. Aviña-Cervantes, Ivan M. Amaya-Contreras, C. R. Correa-Cely

引用次数: 2

Elder Falls Detection Based on Artificial Neural Networks 基于人工神经网络的老年人跌倒检测

2015 Fourteenth Mexican International Conference on Artificial Intelligence (MICAI) Pub Date : 2015-10-25 DOI: 10.1109/MICAI.2015.41

Marcelo Vidigal, M. Lima, A. A. Neto

引用次数: 4

Resource Building and Parts-of-Speech (POS) Tagging for the Mizo Language 米佐语的资源构建与词性标注

2015 Fourteenth Mexican International Conference on Artificial Intelligence (MICAI) Pub Date : 2015-10-25 DOI: 10.1109/MICAI.2015.7

Partha Pakray, A. Pal, Goutam Majumder, Alexander Gelbukh

引用次数: 17

Automating the Creation of Speech Recognition Systems for Under-resourced Languages 为资源不足的语言自动创建语音识别系统

2015 Fourteenth Mexican International Conference on Artificial Intelligence (MICAI) Pub Date : 2015-10-25 DOI: 10.1109/MICAI.2015.11

A. Khusainov, Dzhavdet Suleymanov

引用次数: 0

A New Method for Clustering-Based Channel Assignment for Multi-channel Multi-radio Wireless Mesh Networks 基于聚类的多通道多无线电无线Mesh网络信道分配新方法

2015 Fourteenth Mexican International Conference on Artificial Intelligence (MICAI) Pub Date : 2015-10-25 DOI: 10.1109/MICAI.2015.36

Hamid Alishavandi, H. Parvin, H. Ghaffarian

引用次数: 0

Building a Diverse Ensemble for Classification 构建用于分类的多样化集成

2015 Fourteenth Mexican International Conference on Artificial Intelligence (MICAI) Pub Date : 2015-10-25 DOI: 10.1109/MICAI.2015.28

A. Aminsharifi, Shima Pouyesh, H. Parvin

{"title":"Building a Diverse Ensemble for Classification","authors":"A. Aminsharifi, Shima Pouyesh, H. Parvin","doi":"10.1109/MICAI.2015.28","DOIUrl":"https://doi.org/10.1109/MICAI.2015.28","url":null,"abstract":"Pattern recognition systems are widely used in a host of different fields. Due to some reasons such as lack of knowledge about a method based on which the best classifier is detected for any arbitrary problem, and thanks to significant improvement in accuracy, researchers turn to ensemble methods in almost every task of pattern recognition. Classification as a major task in pattern recognition, have been subject to this transition. The classifier ensemble which uses a number of base classifiers is considered as meta-classifier to learn any classification problem in pattern recognition. Although some researchers think they are better than single classifiers, they will not be better if some conditions are not met. The most important condition among them is diversity of base classifiers. Generally in design of multiple classifier systems, the more diverse the results of the classifiers, the more appropriate the aggregated result. It has been shown that the necessary diversity for the ensemble can be achieved by manipulation of dataset features, manipulation of data points in dataset, different sub-samplings of dataset, and usage of different classification algorithms. We also propose a new method of creating this diversity. We use Linear Discriminante Analysis to manipulate the data points in dataset. Although the classifier ensemble produced by proposed method may not always outperform all of its base classifiers, it always possesses the diversity needed for creation of an ensemble, and consequently it always outperforms all of its base classifiers on average.","PeriodicalId":448255,"journal":{"name":"2015 Fourteenth Mexican International Conference on Artificial Intelligence (MICAI)","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125143672","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1