2015 IEEE 2nd International Conference on Cybernetics (CYBCONF)最新文献

Information extraction methods for text documents in a Cognitive Integrated Management Information System 认知集成管理信息系统中文本文档的信息提取方法

2015 IEEE 2nd International Conference on Cybernetics (CYBCONF) Pub Date : 2015-06-24 DOI: 10.1109/CYBConf.2015.7175948

Marcin Hernes

引用次数: 2

2015 IEEE 2nd International Conference on Cybernetics (CYBCONF) Pub Date : 2015-06-24 DOI: 10.1109/CYBConf.2015.7175932

Duygu Dede Sener, H. Oğul

引用次数: 0

A Chinese dishes recommendation algorithm based on personal taste 一种基于个人口味的中餐推荐算法

2015 IEEE 2nd International Conference on Cybernetics (CYBCONF) Pub Date : 2015-06-24 DOI: 10.1109/CYBConf.2015.7175946

Ningxuan He, Meng Liu, Fang Zhao

引用次数: 6

Estimation of the mechanical state variables of the two-mass system using fuzzy adaptive Kalman filter - Experimental study 用模糊自适应卡尔曼滤波估计双质量系统的力学状态变量——实验研究

2015 IEEE 2nd International Conference on Cybernetics (CYBCONF) Pub Date : 2015-06-24 DOI: 10.1109/CYBConf.2015.7175977

Krzysztof Drozdz

引用次数: 6

Modifications of most expressive feature reordering criteria for supervised kernel Principal Component Analysis 有监督核主成分分析中最具表现力特征重排序准则的改进

2015 IEEE 2nd International Conference on Cybernetics (CYBCONF) Pub Date : 2015-06-24 DOI: 10.1109/CYBConf.2015.7175986

Krzysztof Adamiak, P. Duch, Dominik Zurek, K. Slot

引用次数: 2

Model-based controller for ship track-keeping using neural network 基于模型的神经网络船舶航迹保持控制器

2015 IEEE 2nd International Conference on Cybernetics (CYBCONF) Pub Date : 2015-06-24 DOI: 10.1109/CYBConf.2015.7175928

K. Kula

引用次数: 14

Reacting to different types of concept drift with adaptive and incremental one-class classifiers 用自适应增量单类分类器对不同类型的概念漂移作出反应

2015 IEEE 2nd International Conference on Cybernetics (CYBCONF) Pub Date : 2015-06-24 DOI: 10.1109/CYBConf.2015.7175902

B. Krawczyk, Michal Wozniak

{"title":"Reacting to different types of concept drift with adaptive and incremental one-class classifiers","authors":"B. Krawczyk, Michal Wozniak","doi":"10.1109/CYBConf.2015.7175902","DOIUrl":"https://doi.org/10.1109/CYBConf.2015.7175902","url":null,"abstract":"Modern computer systems generate massive amounts of data in real-time. We have come to the age of big data, where the amount of information exceeds the perceptive abilities of any human being. Frequently the massive data collections arrive over time, in the form of a data stream. Not only the volume and velocity of data poses a challenge for machine learning systems, but also its variability. Such an environment may have non-stationary properties, i.e. change its characteristic over time. This phenomenon is known as concept drift, and is considered as one of the main challenges for moder learning systems. In this paper, we propose to investigate different methods for handling concept drift with adaptive soft one-class classifiers. One-class classification is a promising direction in data stream analytics, as it allows for a novelty detection, data description and learning with limited access to class labels. We describe an adaptive model of Weighted One-Class Support Vector Machine, augmented with mechanisms for incremental learning and forgetting. These allow for our models to swiftly adapt to changes in data, without any need for a dedicated drift detector. We carry out an experimental analysis of the behavior of our method with different forgetting rates for various types of concept drift. Additionally, we compare our classifier with state-of-the-art one-class methods for streaming data. We observe, that our adaptive soft one-class model can efficiently handle different types of concept drifts, while delivering a highly satisfactory accuracy for streaming data.","PeriodicalId":177233,"journal":{"name":"2015 IEEE 2nd International Conference on Cybernetics (CYBCONF)","volume":"78 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126250715","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: 8

Contextual approach to reasoning with rules 用规则推理的上下文方法

2015 IEEE 2nd International Conference on Cybernetics (CYBCONF) Pub Date : 2015-06-24 DOI: 10.1109/CYBConf.2015.7175914

A. Waloszek, W. Waloszek

引用次数: 0

Tool health monitoring using airborne acoustic emission and a PSO-optimized neural network 利用机载声发射和pso优化的神经网络进行工具健康监测

2015 IEEE 2nd International Conference on Cybernetics (CYBCONF) Pub Date : 2015-06-24 DOI: 10.1109/CYBConf.2015.7175945

T. Zafar, K. Kamal, Rohit Kumar, Z. Sheikh, S. Mathavan, U. Ali

引用次数: 2

Problem of efficient initialization of large Self-Organizing Maps implemented in the CMOS technology 利用CMOS技术实现的大型自组织映射的高效初始化问题

2015 IEEE 2nd International Conference on Cybernetics (CYBCONF) Pub Date : 2015-06-24 DOI: 10.1109/CYBConf.2015.7175903

M. Kolasa, R. Dlugosz, W. Pedrycz

{"title":"Problem of efficient initialization of large Self-Organizing Maps implemented in the CMOS technology","authors":"M. Kolasa, R. Dlugosz, W. Pedrycz","doi":"10.1109/CYBConf.2015.7175903","DOIUrl":"https://doi.org/10.1109/CYBConf.2015.7175903","url":null,"abstract":"Initialization of neuron weights is one of key problems in artificial neural networks (ANNs). This problem is particularly important in ANNs implemented as Application Specific Integrated Circuits (ASICs), where the number of the weights becomes large. When ANNs are implemented in software, the weights can be easily programmed. In contrast, in parallel systems of this type realized as ASICs it is necessary to provide programming and addressing lines to each weight that causes a large increase in the complexity of such designs. In this paper we present investigations that demonstrate that Self-Organizing Maps (SOMs) in many situations may be trained without the initialization (with zeroed weights). We present example results of several thousands simulations for different topologies of the SOM, for different neighborhood functions and two distance measures between the learning patterns and particular neurons in the input data space. Simulations were performed for zero initial values, for small values (up to 1 % of full scale range) and for neurons randomly distributed over the overall input data space. The results are comparable that allows to reduce the complexity of the SOM implemented in the CMOS technology.","PeriodicalId":177233,"journal":{"name":"2015 IEEE 2nd International Conference on Cybernetics (CYBCONF)","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132677794","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: 3