We are honored to welcome you to the 2nd International Workshop on Advanced Analyt-ics and Learning on Temporal Data (AALTD), which is held in Riva del Garda, Italy, on September 19th, 2016, co-located with The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD 2016). The aim of this workshop is to bring together researchers and experts in machine learning, data mining, pattern analysis and statistics to share their challenging issues and advance researches on temporal data analysis. Analysis and learning from temporal data cover a wide scope of tasks including learning metrics, learning representations, unsupervised feature extraction, clustering and classification. This volume contains the conference program, an abstract of the invited keynotes and the set of regular papers accepted to be presented at the conference. Each of the submitted papers was reviewed by at least two independent reviewers, leading to the selection of eleven papers accepted for presentation and inclusion into the program and these proceedings. The contributions are given by the alphabetical order, by surname. The keynote given by Marco Cuturi on "Regularized DTW Divergences for Time Se-ries" focuses on the definition of alignment kernels for time series that can later be used at the core of standard machine learning algorithms. The one given by Tony Bagnall on "The Great Time Series Classification Bake Off" presents an important attempt to experimentally compare performance of a wide range of time series classifiers, together with ensemble classifiers that aim at combining existing classifiers to improve classification quality. Accepted papers spanned from innovative ideas on analytic of temporal data, including promising new approaches and covering both practical and theoretical issues. We wish to thank the ECML PKDD council members for giving us the opportunity to hold the AALTD workshop within the framework of the ECML/PKDD Conference and the members of the local organizing committee for their support. The organizers of the AALTD conference gratefully thank the financial support of the Université de Rennes 2, MODES and Universidade da Coruña. Last but not least, we wish to thank the contributing authors for the high quality works and all members of the Reviewing Committee for their invaluable assistance in the iii selection process. All of them have significantly contributed to the success of AALTD 2106. We sincerely hope that the workshop participants have a great and fruitful time at the conference.
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声学数据提供从生物学和通信到海洋和地球科学等领域的科学和工程见解。我们调查了机器学习(ML)的进步和变革潜力,包括声学领域的深度学习。 ML是用于自动检测和利用模式印度的广泛的统计技术家族。相对于传统的声学和信号处理,ML是数据驱动的。给定足够的训练数据,ML可以发现特征之间的复杂关系。通过大量的训练数据,ML candiscover模型描述复杂的声学现象,如人类语音和混响。声学中的ML正在迅速发展,具有令人瞩目的成果和未来的重大前景。我们首先介绍ML,然后在五个声学研究领域强调MLdevelopments:语音处理中的源定位,海洋声学中的源定位,生物声学,地震探测和日常场景中的环境声音。
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新颖性检测是识别与训练集明显不同的数据异常的无监督问题。新颖性检测是机器学习中的经典挑战之一,也是欺诈检测,入侵检测,医疗诊断,数据清理和故障预防等几个研究领域的核心组成部分。虽然设计了许多算法来解决这个问题,但大多数方法仅适用于模拟连续数值数据。处理由混合类型特征(例如数值和分类数据)或描述离散事件序列的时间数据集组成的数据集是一项具有挑战性的任务。除了支持的数据类型之外,有效新颖性检测方法的关键标准是能够准确地将新颖性与标称样本分离,可解释性,可扩展性以及对位于训练数据中的异常的鲁棒性。在本文中,我们研究了解决这些问题的新方法。特别地,我们提出(i)混合型数据的新颖性检测方法的实验比较(ii)序列数据的新颖检测方法的实验比较,(iii)基于Dirichlet过程混合和指数的混合型数据的概率非参数奇异检测方法。 - 家庭分布和(iv)基于自动编码器的新奇检测模型,其编码器/解码器被建模为深度高斯过程。
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在过去十年中,对于文本,DNA和少数其他数据类型的所有对 - 相似性 - 搜索(或自连接)的研究已经进行了大量研究,并且这些系统已经应用于许多不同的数据挖掘问题。然而,令人惊讶的是,在解决时间序列子序列的这个问题上几乎没有取得任何进展。在本文中,我们引入了一种近乎通用的时间序列数据挖掘工具,称为矩阵轮廓,它解决了所有对 - 相似性 - 搜索问题,并以易于访问的方式缓存输出。该算法不仅具有参数,精确和可扩展性,而且适用于单维和多维时间序列。通过在矩阵轮廓之上构建时间序列数据挖掘方法,可以有效地解决许多时间序列数据挖掘任务(例如,主题发现,不和谐发现,形状发现,语义分割和聚类)。因为相同的矩阵轮廓可以由多样性共享一组时间序列数据挖掘方法,矩阵简档是多功能和计算一次使用多次数据结构。我们展示了矩阵轮廓在许多时间序列数据挖掘问题中的实用性,包括motifdiscovery,不和谐发现,弱标记时间序列分类,以及各种领域的代表性学习,如地震学,昆虫学,音乐处理,生物信息学,人类活动监测,电力需求监测,和医学。我们希望矩阵配置文件不是结束,而是更多时间序列数据挖掘项目的开始。
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Time Series Classification (TSC) is an important and challenging problem in data mining. With the increase of time series data availability, hundreds of TSC algorithms have been proposed. Among these methods, only a few have considered Deep Neural Networks (DNNs) to perform this task. This is surprising as deep learning has seen very successful applications in the last years. DNNs have indeed revolutionized the field of computer vision especially with the advent of novel deeper architectures such as Residual and Convolutional Neural Networks. Apart from images, sequential data such as text and audio can also be processed with DNNs to reach state-of-the-art performance for document classification and speech recognition. In this article, we study the current state-of-the-art performance of deep learning algorithms for TSC by presenting an empirical study of the most recent DNN architectures for TSC. We give an overview of the most successful deep learning applications in various time series domains under a unified taxonomy of DNNs for TSC. We also provide an open source deep learning framework to the TSC community where we implemented each of the compared approaches and evaluated them on a univariate TSC benchmark (the UCR/UEA archive) and 12 multivariate time series datasets. By training 8,730 deep learning models on 97 time series datasets, we propose the most exhaustive study of DNNs for TSC to date.
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We introduce a very general method for high dimensional classification, based on careful combination of the results of applying an arbitrary base classifier to random projections of the feature vectors into a lower dimensional space. In one special case that we study in detail, the random projections are divided into disjoint groups, and within each group we select the projection yielding the smallest estimate of the test error. Our random-projection ensemble classifier then aggregates the results of applying the base classifier on the selected projections, with a data-driven voting threshold to determine the final assignment. Our theoretical results elucidate the effect on performance of increasing the number of projections. Moreover, under a boundary condition that is implied by the sufficient dimension reduction assumption, we show that the test excess risk of the random-projection ensemble classifier can be controlled by terms that do not depend on the original data dimension and a term that becomes negligible as the number of projections increases. The classifier is also compared empirically with several other popular high dimensional classifiers via an extensive simulation study, which reveals its excellent finite sample performance.
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近年来,复杂文档和文本的数量呈指数增长,需要更深入地了解机器学习方法,才能在许多应用程序中准确地对文本进行分类。许多机器学习方法在自然语言处理方面取得了超越的成果。这些学习算法的成功依赖于它们能够理解数据中的复杂模型和非线性关系。然而,为文本分类找到合适的结构,体系结构和技术对研究人员来说是一个挑战。在本文中,讨论了文本分类算法的简要概述。本概述涵盖了不同的文本特征提取,降维方法,现有算法和技术以及评估方法。最后,讨论了每种技术的局限性及其在现实问题中的应用。
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推文,博客文章或产品评论的情感极性变得极具吸引力,并在推荐系统,市场预测,商业智能等方面得到应用。深度学习技术正在成为分析此类文本的最佳表现者。然而,在文本挖掘和文本极化分析中有效地使用深度神经网络需要解决几个问题。首先,需要为深度神经网络提供大小和正确标记的数据集。其次,关于字嵌入向量的使用存在各种不确定性:它们是否应该从用于训练模型的相同数据集生成,还是更适合从大型和流行的集合中获取它们?第三,为了简化模型创建,使通用神经网络架构有效并且可以适应各种文本,封装大部分设计复杂性是很方便的。本文针对上述问题,提出了利用神经网络进行情感分析和实现最新技术成果的方法论实践见解。关于第一个问题,探讨了各种众包替代方案的有效性,并利用社交标准创建了双胞胎大小和情感标记的歌曲数据集。为了解决第二个问题,进行了一系列具有各种内容和域的大文本集的实验,尝试各种参数的插入。关于第三个问题,进行了一系列涉及卷积和最大汇集神经层的实验。将单词,双字母和三元组的卷积与几个堆栈中的区域最大汇集层相结合产生了最好的结果。派生体系结构在电影,商业和产品评论的情感极性分析中实现了竞争性表现。
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深度学习提出了希望和期望,作为许多应用程序的一般解决方案;事实证明它已被证明是有效的,但它也显示出对大量数据的强烈依赖性。幸运的是,已经证明,即使数据稀缺,也可以通过重复使用priorknowledge来训练成功的模型。因此,在最广泛的定义中,开发转移学习技术是部署有效和准确的智能系统的关键因素。本文将重点研究一系列适用于视觉目标识别任务的转移学习方法,特别是图像分类。转移学习是一个通用术语,并且特定设置已经给出了特定的名称:当学习者只能访问来自目标域的标记数据和来自不同域(源)的标记数据时,问题被称为“无监督域适应”。 (DA)。这项工作的第一部分将集中在这个设置的三种方法:其中一种方法涉及特征,一种是图像,而第三种方法同时使用两种。第二部分将重点关注机器人感知的现实生活问题,特别是RGB-D识别。机器人平台通常不仅限于色彩感知;他们经常带着Depthcamera。不幸的是,深度模态很少用于视觉识别,因为缺乏预先训练的模型,从中可以传输并且很少有数据从头开始。将提出两种处理这种情况的方法:一种使用合成数据,另一种利用跨模态转移学习。
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在本文中,我们报告了我们对文本数据密集分布表示的研究结果。我们提出了两种新颖的神经模型来学习这种表征。第一个模型学习文档级别的表示,而第二个模型学习单词级表示。对于文档级表示,我们提出二进制段落向量:用于学习文本文档的二进制表示的神经网络模型,其可用于快速文档检索。我们对这些模型进行了全面评估,并证明它们在信息检索任务中的表现优于该领域的开创性方法。我们还报告了强有力的结果转换学习设置,其中我们的模型在通用textcorpus上训练,然后用于从特定于域的数据集推断文档的代码。与先前提出的方法相反,二进制段落矢量模型直接从原始文本数据学习嵌入。对于词级表示,我们提出消歧Skip-gram:用于学习多义词嵌入的神经网络模型。通过该模型学习的表示可以用于下游任务,例如词性标记或语义关系的识别。在单词意义上感应任务Disambiguated Skip-gram在三个基准测试数据集上优于最先进的模型。我们的模型具有优雅的概率解释。此外,与以前的这种模型不同,它在所有参数方面都是不同的,并且可以用反向传播进行训练。除了定量结果,我们还提出消除歧义的Skip-gram的定性评估,包括选定的词义嵌入的二维可视化。
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Automatic Speech Recognition (ASR) has historically been a driving force behind many machine learning (ML) techniques, including the ubiquitously used hidden Markov model, discriminative learning, structured sequence learning, Bayesian learning, and adaptive learning. Moreover, ML can and occasionally does use ASR as a large-scale, realistic application to rigorously test the effectiveness of a given technique, and to inspire new problems arising from the inherently sequential and dynamic nature of speech. On the other hand, even though ASR is available commercially for some applications, it is largely an unsolved problem-for almost all applications, the performance of ASR is not on par with human performance. New insight from modern ML methodology shows great promise to advance the state-of-the-art in ASR technology. This overview article provides readers with an overview of modern ML techniques as utilized in the current and as relevant to future ASR research and systems. The intent is to foster further cross-pollination between the ML and ASR communities than has occurred in the past. The article is organized according to the major ML paradigms that are either popular already or have potential for making significant contributions to ASR technology. The paradigms presented and elaborated in this overview include: generative and discriminative learning; supervised, unsupervised, semi-supervised, and active learning; adaptive and multi-task learning; and Bayesian learning. These learning paradigms are motivated and discussed in the context of ASR technology and applications. We finally present and analyze recent developments of deep learning and learning with sparse representations, focusing on their direct relevance to advancing ASR technology.
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Clustering is the unsupervised classification of patterns (observations, data items, or feature vectors) into groups (clusters). The clustering problem has been addressed in many contexts and by researchers in many disciplines; this reflects its broad appeal and usefulness as one of the steps in exploratory data analysis. However, clustering is a difficult problem combinatorially, and differences in assumptions and contexts in different communities has made the transfer of useful generic concepts and methodologies slow to occur. This paper presents an overview of pattern clustering methods from a statistical pattern recognition perspective, with a goal of providing useful advice and references to fundamental concepts accessible to the broad community of clustering practitioners. We present a taxonomy of clustering techniques, and identify cross-cutting themes and recent advances. We also describe some important applications of clustering algorithms such as image segmentation, object recognition, and information retrieval.
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今天的电信网络已成为大量广泛异构数据的来源。该信息可以从网络交通轨迹,网络警报,信号质量指示符,用户行为数据等中检索。需要高级数学工具从这些数据中提取有意义的信息,并从网络生成的数据中做出与网络的正常运行有关的决策。在这些数学工具中,机器学习(ML)被认为是执行网络数据分析和实现自动网络自配置和故障管理的最具前景的方法之一。 ML技术在光通信网络领域的应用受到光网络在最近几年所面临的网络复杂性的前所未有的增长的推动。这种复杂性的增加是由于引入了一系列可调和相互依赖的系统参数(例如,路由配置,调制格式,符号率,编码方案等),这些参数通过使用相干传输/接收技术,高级数字信号处理和光纤传播中非线性效应的补偿。在本文中,我们概述了ML在光通信和网络中的应用。我们对涉及该主题的相关文献进行分类和调查,并且我们还为对该领域感兴趣的研究人员和从业者提供了ML的入门教程。虽然最近出现了大量的研究论文,但ML光学网络的应用仍处于起步阶段:为了激发这一领域的进一步工作,我们总结了该论文提出了新的可能的研究方向。
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Concept drift primarily refers to an online supervised learning scenario when the relation between the input data and the target variable changes over time. Assuming a general knowledge of supervised learning in this paper we characterize adaptive learning process, categorize existing strategies for handling concept drift, discuss the most representative, distinct and popular techniques and algorithms, discuss evaluation methodology of adaptive algorithms, and present a set of illustrative applications. This introduction to the concept drift adaptation presents the state of the art techniques and a collection of benchmarks for researchers , industry analysts and practitioners. The survey aims at covering the different facets of concept drift in an integrated way to reflect on the existing scattered state-of-the-art.
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Recommender systems have become essential tools for users to navigate the plethora of content in the online world. Collaborative filtering-a broad term referring to the use of a variety, or combination, of machine learning algorithms operating on user ratings-lies at the heart of recommender systems' success. These algorithms have been traditionally studied from the point of view of how well they can predict users' ratings and how precisely they rank content; state of the art approaches are continuously improved in these respects. However, a rift has grown between how filtering algorithms are investigated and how they will operate when deployed in real systems. Deployed systems will continuously be queried for personalised recommendations; in practice, this implies that system administrators will iteratively retrain their algorithms in order to include the latest ratings. Collaborative filtering research does not take this into account: algorithms are improved and compared to each other from a static viewpoint, while they will be ultimately deployed in a dynamic setting. Given this scenario, two new problems emerge: current filtering algorithms are neither (a) designed nor (b) evaluated as algorithms that must account for time. This thesis addresses the divergence between research and practice by examining how collaborative filtering algorithms behave over time. Our contributions include: 1. A fine grained analysis of temporal changes in rating data and user/item similarity graphs that clearly demonstrates how recommender system data is dynamic and constantly changing. 2. A novel methodology and time-based metrics for evaluating collaborative filtering over time, both in terms of accuracy and the diversity of top-N recommendations. 3. A set of hybrid algorithms that improve collaborative filtering in a range of different scenarios. These include temporal-switching algorithms that aim to promote either accuracy or diversity; parameter update methods to improve temporal accuracy; and re-ranking a subset of users' recommendations in order to increase diversity. 4. A set of temporal monitors that secure collaborative filtering from a wide range of different temporal attacks by flagging anomalous rating patterns. We have implemented and extensively evaluated the above using large-scale sets of user ratings; we further discuss how this novel methodology provides insight into dimensions of recommender systems that were previously unexplored. We conclude that investigating collaborative filtering from a temporal perspective is not only more suitable to the context in which recommender systems are deployed, but also opens a number of future research opportunities.
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In this dissertation, the problem of learning from highly imbalanced data is studied. Imbalance data learning is of great importance and challenge in many real applications. Dealing with a minority class normally needs new concepts, observations and solutions in order to fully understand the underlying complicated models. We try to systematically review and solve this special learning task in this dis-sertation. We propose a new ensemble learning framework-Diversified Ensemble Classifiers for Imbal-anced Data Learning (DECIDL), based on the advantages of existing ensemble imbalanced learning strategies. Our framework combines three learning techniques: a) ensemble learning, b) artificial example generation, and c) diversity construction by reversely data re-labeling. As a meta-learner, DECIDL utilizes general supervised learning algorithms as base learners to build an ensemble committee. We create a standard benchmark data pool, which contains 30 highly skewed sets with diverse characteristics from different domains, in order to facilitate future research on imbalance data learning. We use this benchmark pool to evaluate and compare our DECIDL framework with several ensemble learning methods, namely under-bagging, over-bagging, SMOTE-bagging, and AdaBoost. Extensive experiments suggest that our DECIDL framework is comparable with other methods. The data sets, experiments and results provide a valuable knowledge base for future research on imbalance learning. We develop a simple but effective artificial example generation method for data balancing. Two new methods DBEG-ensemble and DECIDL-DBEG are then designed to improve the power of imbalance learning. Experiments show that these two methods are comparable to the state-of-the-art methods, e.g., GSVM-RU and SMOTE-bagging. Furthermore, we investigate learning on imbalanced data from a new angle-active learning. By combining active learning with the DECIDL framework, we show that the newly designed Active-DECIDL method is very effective for imbalance learning, suggesting the DECIDL framework is very robust and flexible. Lastly, we apply the proposed learning methods to a real-world bioinformatics problem-protein methylation prediction. Extensive computational results show that the DECIDL method does perform very well for the imbalanced data mining task. Importantly, the experimental results have confirmed our new contributions on this particular data learning problem. iv DEDICATION To my parents, To my grandfather, who cherished me since a kid, To my grandmother, who loved me the most, but I don't know her name, To my love. v ACKNOWLEDGEMENTS
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