第十一届中国机器学习会议

第十一届中国机器学习会议（CCML2008）由中国人工智能学会机器学习专业委员会和中国计算机学会人工智能与模式识别专业委员会联合主办,大连海事大学承办．该系列会议每两年举行一次,现已成为国内机器学习界最主要的学术活动．此次会议将为机器学习及相关研究领域的学者交流最新研究成果、进行广泛的学术讨论提供便利,并且将邀请国内机器学习领域的著名学者做精彩报告．     

多媒体技术研究:2017——记忆驱动的媒体学习与创意

目的 借鉴大脑的工作机理来发展人工智能是当前人工智能发展的重要方向之一。注意力与记忆在人的认知理解过程中扮演了重要的角色。由于"端到端"深度学习在识别分类等任务中表现了优异性能,因此如何在深度学习模型中引入注意力机制和外在记忆结构,以挖掘数据中感兴趣的信息和有效利用外来信息,是当前人工智能研究的热点。方法 本文以记忆和注意力等机制为中心,介绍了这些方面的3个代表性工作,包括神经图灵机、记忆网络和可微分神经计算机。在这个基础上,进一步介绍了利用记忆网络的研究工作,其分别是记忆驱动的自动问答、记忆驱动的电影视频问答和记忆驱动的创意（文本生成图像）,并对国内外关于记忆网络的研究进展进行了比较。结果 调研结果表明：1）在深度学习模型中引入注意力机制和外在记忆结构,是当前人工智能研究的热点;2）关于记忆网络的研究越来越多。国内外关于记忆网络的研究正在蓬勃发展,每年发表在机器学习与人工智能相关的各大顶级会议上的论文数量正在逐年攀升;3）关于记忆网络的研究越来越热。不仅每年发表的论文数量越来越多,且每年的增长趋势并没有放缓,2015年增长了9篇,2016年增长了4篇,2017年增长了9篇,2018年增长了14篇;4）基于记忆驱动的手段和方法十分通用。记忆网络已成功地运用于自动问答、视觉问答、物体检测、强化学习、文本生成图像等领域。结论 数据驱动的机器学习方法已成功运用于自然语言、多媒体、计算机视觉、语音等领域,数据驱动和知识引导将是人工智能未来发展的趋势之一。     

构建智能平台 打造精品期刊《智能系统学报》双月刊

第十一届中国机器学习会议（CCML2008）由中国人工智能学会机器学习专业委员会和中国计算机学会人工智能与模式识别专业委员会联合主办，大连海事大学承办，该系列会议每两年举行一次，现已成为国内机器学习界最主要的学术活动．此次会议将为机器学习及相关研究领域的学者交流最新研究成果、进行广泛的学术讨论提供便利，并且将邀请罔内机器学习领域的著名学者做精彩报告．     

第十届中国机器学习会议

第十届中国机器学习会议（CCML2006）由中国人工智能学会机器学习专业委员会和中国计算机学会模式识别与人工智能专业委员会联合主办,海南大学承办,海南软件学院协办．该系列会议每两年举行一次,现已成为国内机器学习界最主要的学术活动．此次会议将为机器学习及相关研究领域的学者交流最新研究成果、进行广泛的学术讨论提供便利,并且将邀请国内机器学习领域的著名学者做精彩报告．     

第六届欧洲人工智能会议论文综述

欧洲人工智能会议—ECAI 是两年一度的世界性人工智能学术研究会议,其规模与世界人工智能会议相当。第六届 ECAI 于1984年9月6日—9日在意大利的比萨举行。22个国家参加了本届会议,共录用154篇论文,其中,英国30篇,法国28篇,美国26篇,西德14篇,意大     

第二届中国人工智能联合学术会议纪要

第二届中国人工智能联合学术会议(CJCAI’92)经过两年多时间的筹备,于1992年11月23日至11月25日在杭州浙江大学召开,出席会议的代表共120人,会议收到了来自全国各地学者的近二百篇论文,经过大会程序委员会的严格的初审与终审,最后录用于论文集的论文共69篇,这些论文内容涉及到AI的基础理论,AI语言,AI体系结构,知识表示与知识系统,智能计算机辅助教学,自动推理,机器学习与知识获取,模式识别与神经网络,人工智能应用等人工智能的各个方面,这些论文有较高的学术价值,有些成果还有良好的应用前景。     

中国人工智能学会第五届学术年会在京召开

中国人工智能学会(CAAI)第五届学术年会于1987年4月6日至10日在北京钢铁学院召开,会议收到论文189篇,经专家审定录用112篇在会上宣读。论文的内容:人工智能现状与展望(10篇)、专家系统、知识工程(31篇)、智能控制与智能管理(22篇)、计算机视觉与模式识别(27篇)、人工智能其他问题(2Z篇)。会议中还召开了理事会,选举了新的理事长和副理事长。     

人工智能的新浪潮——深度学习

深度学习（Deep Learing）作为一种基于人工神经网络的无监督学习方法,是近年来兴起的一种新的混合机器学习模型,现成为人工智能领域中炙手可热的研究技术。深度学习带来了机器学习的一个新浪潮,受到学术界和工业界的广泛重视,并带来大数据的深度学习时代。本文结合大数据时代的具体需求,详细阐述了深度学习的发展和应用,突出了其在人工智能领域的重要地位。     

第十届中国机器学习会议征文通知2006年10月13～15日,海口

第十届中国机器学习会议(CCML2006)由中国人工智能学会机器学习专业委员会和中国计算机学会模式识别与人工智能专业委员会联合主办,海南大学承办,海南软件学院协办.该系列会议每两年举行一次,现已成为国内机器学习界最主要的学术活动.此次会议将为机器学习及相关研究领域的学者交流最新研究成果、进行广泛的学术讨论提供便利,并且将邀请国内机器学习领域的著名学者做精彩报告.征稿范围(征求但不限于如下主题)·机器学习的新理论、新技术与新应用·人类学习的计算模型·计算学习理论·监督学习·非监督学习·强化学习·多示例学习·半监督…     

“第十届中国机器学习会议”召开《计算机辅助工程》出版会议专刊

2006年10月13—15日，第十届中国机器学习会议（CCML2006）在海南大学召开。本次会议由中国人工智能学会机器学习专业委员会、中国计算机学会人工智能与模式识别专业委员会联合主办，海南大学承办，海南软件学院和北京会务通培训中心协办。国内机器学习领域的著名学者和专家做了精彩报告，参会人员广泛交流最新的研究成果，并就机器学习的热门专题进行深刻的学术讨论。     

量子机器学习

人工智能和量子物理是上世纪发展起来的两个截然不同但又影响深远的学科.近年来,它们在数据科学方面的结合引起了学术界的高度关注,形成了量子机器学习这个新兴领域.利用量子态的叠加性,量子机器学习有望通过量子并行解决目前机器学习中数据量大,训练过程慢的困难,并有望从量子物理的角度提出新的学习模型.目前该领域的研究还处于探索阶段,涵盖内容虽然广泛,但还缺乏系统的梳理.本文将从数据和算法角度总结量子机器学习与经典机器学习的不同,以及其中涉及的关键加速技巧,针对数据结构(数字型、模拟型),计算技巧(相位估计、Grover搜索、内积计算),基础算法(求解线性系统、主成分分析、梯度算法),学习模型(支持向量机、近邻法、感知器、玻尔兹曼机)等4个方面对现有研究成果进行综述,并建议一些可能的研究方向,供本领域的研究人员参考.     

Comparison of different machine learning techniques on location extraction by utilizing geo-tagged tweets: A case study

In emergencies, Twitter is an important platform to get situational awareness simultaneously. Therefore, information about Twitter users’ location is a fundamental aspect to understand the disaster effects. But location extraction is a challenging task. Most of the Twitter users do not share their locations in their tweets. In that respect, there are different methods proposed for location extraction which cover different fields such as statistics, machine learning, etc. This study is a sample study that utilizes geo-tagged tweets to demonstrate the importance of the location in disaster management by taking three cases into consideration. In our study, tweets are obtained by utilizing the “earthquake” keyword to determine the location of Twitter users. Tweets are evaluated by utilizing the Latent Dirichlet Allocation (LDA) topic model and sentiment analysis through machine learning classification algorithms including the Multinomial and Gaussian Naïve Bayes, Support Vector Machine (SVM), Decision Tree, Random Forest, Extra Trees, Neural Network, k Nearest Neighbor (kNN), Stochastic Gradient Descent (SGD), and Adaptive Boosting (AdaBoost) classifications. Therefore, 10 different machine learning algorithms are applied in our study by utilizing sentiment analysis based on location-specific disaster-related tweets by aiming fast and correct response in a disaster situation. In addition, the effectiveness of each algorithm is evaluated in order to gather the right machine learning algorithm. Moreover, topic extraction via LDA is provided to comprehend the situation after a disaster. The gathered results from the application of three cases indicate that Multinomial Naïve Bayes and Extra Trees machine learning algorithms give the best results with an F-measure value over 80%. The study aims to provide a quick response to earthquakes by applying the aforementioned techniques.     

Research intelligence involving information retrieval – An example of conferences and journals

This paper reports a work that was intended to reveal the connection between topics investigated by conference papers and journal papers. This work selected hundreds of papers in data mining and information retrieval from well-known databases and showed that the topics covered by conference papers in a year often leads to similar topics covered by journal papers in the subsequent year and vice versa. This study used some existing algorithms and combination of these algorithms to proposed a new detective procedure for the researchers to detect the new trend and get the academic intelligence from conferences and journals.The goal of this research is fourfold: First, the research investigates if the conference papers’ themes lead the journal papers’. Second, the research examines how the new research themes can be identified from the conference papers. Third, the research looks at a specific area such as information retrieval and data mining as an illustration. Fourth, the research studies any inconsistencies of the correlation between the conference papers and the journal papers.This study explores the connections between the academic publications. The methodologies of information retrieval and data mining can be exploited to discover the relationships between published papers among all topics. By discovering the connections between conference papers and journal papers, researchers can improve the effectiveness of their research by identifying academic intelligence.This study discusses how conference papers and journal papers are related. The topics of conference papers are identified to determine whether they represent new trend discussed in journal papers. An automatic examination procedure based on information retrieval and data mining is also proposed to minimize the time and human resources required to predict further research developments. This study develops a new procedure and collects a dataset to verify those problems. Analytical results demonstrate that the conference papers submitted to journals papers are similar each year. Conference papers certainly affect the journal papers published over three years. About 87.23% of data points from papers published in 1991–2007 support our assumption. The research is intended to help researchers identify new trend in their research fields, and focus on the urgent topics. This is particularly valuable for new researchers in their field, or those who wish to perform cross-domain studies.     

机器学习的查询扩展在博客检索中的应用

该文介绍一种新的查询扩展方法,该方法结合了查询扩展技术和机器学习理论。通过机器学习的方法挑选出查询扩展词,以此提高检索结果的性能。对于输入的查询项,首先通过伪反馈技术生成候选扩展词集合,然后使用支持向量机对输入的候选词评分,挑选得分较高的候选词和原始查询项组成一个新的查询项。由于训练这个支持向量机的训练数据较难获得,我们利用评测会议的检索结果和检索工具自动地生成训练数据。这套查询扩展方法的优点在于通过对训练语料的学习,能够对候选扩展词作出更合理的选择。在TREC评测会议组织的观点检索任务中,相对于不采用任何扩展技术的基准系统,该方法提高了MAP指标33.1%。     

A systematic review of software fault prediction studies

This paper provides a systematic review of previous software fault prediction studies with a specific focus on metrics, methods, and datasets. The review uses 74 software fault prediction papers in 11 journals and several conference proceedings. According to the review results, the usage percentage of public datasets increased significantly and the usage percentage of machine learning algorithms increased slightly since 2005. In addition, method-level metrics are still the most dominant metrics in fault prediction research area and machine learning algorithms are still the most popular methods for fault prediction. Researchers working on software fault prediction area should continue to use public datasets and machine learning algorithms to build better fault predictors. The usage percentage of class-level is beyond acceptable levels and they should be used much more than they are now in order to predict the faults earlier in design phase of software life cycle.     

Multi layer ELM-RBF for multi-label learning

Many neural network methods such as ML-RBF and BP-MLL have been used for multi-label classification. Recently, extreme learning machine (ELM) is used as the basic elements to handle multi-label classification problem because of its fast training time. Extreme learning machine based auto encoder (ELM-AE) is a novel method of neural network which can reproduce the input signal as well as auto encoder, but it can not solve the over-fitting problem in neural networks elegantly. Introducing weight uncertainty into ELM-AE, we can treat the input weights as random variables following Gaussian distribution and propose weight uncertainty ELM-AE (WuELM-AE). In this paper, a neural network named multi layer ELM-RBF for multi-label learning (ML-ELM-RBF) is proposed. It is derived from radial basis function for multi-label learning (ML-RBF) and WuELM-AE. ML-ELM-RBF firstly stacks WuELM-AE to create a deep network, and then it conducts clustering analysis on samples features of each possible class to compose the last hidden layer. ML-ELM-RBF has achieved satisfactory results on single-label and multi-label data sets. Experimental results show that WuELM-AE and ML-ELM-RBF are effective learning algorithms.     

基于ELM和证据理论的纹理图像分类

目前纹理图像分类有不同的方法,但对纹理的描述还不够全面,而且当有新方法提取的特征加入时,系统的可扩展性也不够,通用性不好。本文针对上述问题提出了一种将D-S证据理论与极限学习机相结合的决策级融合模型,用来对纹理图像进行分类。采用三种不同方法来提取特征以获得更多更全面的纹理表现形式,并对提取的每种特征向量用极限学习机建立相应的分类器,最后用D-S证据理论在不确定性表示、度量和组合方面有着的优势来进行决策级融合。对于证据理论中基本概率赋值函数(BPAF)难以有效获取的问题,由于极限学习机具有学习速度快,泛化性能好的优点并且产生唯一的最优解的优点,所以利用其来构造其基本概率赋值函数。实验结果表明这种方法比单个分类器具有更高的识别正确率,降低了识别的不确定性。     

Treelet kernel incorporating cyclic,stereo and inter pattern information in chemoinformatics

Chemoinformatics is a research field concerned with the study of physical or biological molecular properties through computer science?s research fields such as machine learning and graph theory. From this point of view, graph kernels provide a nice framework which allows to naturally combine machine learning and graph theory techniques. Graph kernels based on bags of patterns have proven their efficiency on several problems both in terms of accuracy and computational time. Treelet kernel is a graph kernel based on a bag of small subtrees. We propose in this paper several extensions of this kernel devoted to chemoinformatics problems. These extensions aim to weight each pattern according to its influence, to include the comparison of non-isomorphic patterns, to include stereo information and finally to explicitly encode cyclic information into kernel computation.     

Unreal PowerPoint: Immersing PowerPoint presentations in a virtual computer game engine world

At a recent conference on games in education, we made a radical decision to transform our standard presentation of PowerPoint slides and computer game demonstrations into a unified whole, inserting the PowerPoint presentation to the computer game. This opened up various questions relating to learning and teaching theories, which were debated by the conference delegates. In this paper, we reflect on these discussions, we present our initial experiment, and relate this to various theories of learning and teaching. In particular, we consider the applicability of “concept maps” to inform the construction of educational materials, especially their topological, geometrical and pedagogical significance. We supplement this “spatial” dimension with a theory of the dynamic, temporal dimension, grounded in a context of learning processes, such as Kolb’s learning cycle. Finally, we address the multi-player aspects of computer games, and relate this to the theories of social and collaborative learning. This paper attempts to explore various theoretical bases, and so support the development of a new learning and teaching virtual reality approach.