2015 IEEE International Conference on Evolving and Adaptive Intelligent Systems (EAIS)最新文献_第4页

Genetic decision mechanism for reasoning and behaviour generation in adaptive intelligent agents 自适应智能体推理和行为生成的遗传决策机制

2015 IEEE International Conference on Evolving and Adaptive Intelligent Systems (EAIS) Pub Date : 2015-12-01 DOI: 10.1109/EAIS.2015.7368790

Andreea Ion, M. Pătrașcu, Vlad Constantinescu

引用次数: 1

An experimental protocol for the evaluation of open-ended category learning algorithms 评估开放式类别学习算法的实验协议

2015 IEEE International Conference on Evolving and Adaptive Intelligent Systems (EAIS) Pub Date : 2015-12-01 DOI: 10.1109/EAIS.2015.7368779

Aneesh Chauhan, L. Lopes

{"title":"An experimental protocol for the evaluation of open-ended category learning algorithms","authors":"Aneesh Chauhan, L. Lopes","doi":"10.1109/EAIS.2015.7368779","DOIUrl":"https://doi.org/10.1109/EAIS.2015.7368779","url":null,"abstract":"There has been a steady surge in various sub-fields of machine learning where the focus is on developing systems that learn in an open-ended manner. This is particularly visible in the fields of language grounding and data stream learning. These systems are designed to evolve as new data arrive, modifying and adjusting learned categories, as well as, accommodating new categories. Although some of the features of incremental learning are present in open-ended learning, the latter can not be characterized as standard incremental learning. This paper presents and discusses the key characteristics of open-ended learning, differentiating it from the standard incremental approaches. The main contribution of this paper is concerned with the evaluation of these algorithms. Typically, the performance of learning algorithms is assessed using traditional train-test methods, such as holdout, cross-validation etc. These evaluation methods are not suited for applications where environments and tasks can change and therefore the learning system is frequently facing new categories. To address this, a well defined and practical protocol is proposed. The utility of the protocol is demonstrated by evaluating and comparing a set of learning algorithms at the task of open-ended visual category learning.","PeriodicalId":325875,"journal":{"name":"2015 IEEE International Conference on Evolving and Adaptive Intelligent Systems (EAIS)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130095830","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: 4

Credit scoring based on a continuous scale for on-line credit quality control 基于连续尺度的信用评分用于在线信用质量控制

2015 IEEE International Conference on Evolving and Adaptive Intelligent Systems (EAIS) Pub Date : 2015-12-01 DOI: 10.1109/EAIS.2015.7368796

K. Romanyuk

引用次数: 3

Keynote speaker 1: Active online learning 主讲人1:积极的在线学习

2015 IEEE International Conference on Evolving and Adaptive Intelligent Systems (EAIS) Pub Date : 2015-12-01 DOI: 10.1109/EAIS.2015.7368771

A. Bouchachia

{"title":"Keynote speaker 1: Active online learning","authors":"A. Bouchachia","doi":"10.1109/EAIS.2015.7368771","DOIUrl":"https://doi.org/10.1109/EAIS.2015.7368771","url":null,"abstract":"Summary form only given. Over the recent years learning from data streams that evolve over time has been witnessing an ever-increasing interest within research and industry communities. Typically a wide range of applications exploit data streams for different sorts of decision making, including monitoring, industrial processes, internet traffic, surveillance, etc. By their very nature, data streams are usually unlabeled given the high velocity of their generation. Collecting labelled examples become very difficult, delayed, costly and sometimes prone to errors. It is therefore very important to devise mechanisms to optimize the labeling process. Active learning offers a principled and systematic way to selectively choose candidate data examples whose labels are to be queried. The overall goal of active learning is to provide, in the worst case, the same performance as that of passive learning (i.e., relying on random sampling) while using less labeled examples. Obviously, the learner should also be able to accommodate unlabeled and labeled data in an online manner. In this talk we will cover recent work on active learning for data stream classification, which is known as stream-based selective sampling. In this latter, the learner makes immediate query decision for each data example during a single scan of the data stream. Stream-based selective sampling is in particular suitable for applications that demand on-the-fly interactive labelling. It is however difficult, because the learner lacks complete knowledge of the underlying data distribution and because such distribution may suffer dynamic change over time. We will overview active learning for stationary as well as non-stationary evolving data streams. In particular, we will discuss multi-criteria active learning and methods for dealing with data drift using online active learning. We will also highlight some of the typical applications where online active learning is relevant.","PeriodicalId":325875,"journal":{"name":"2015 IEEE International Conference on Evolving and Adaptive Intelligent Systems (EAIS)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127531986","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: 0