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检索条件"任意字段=7th European Conference on Principles and Practice of Knowledge Discovery in Databases"

共 85 条记录，以下是21-30 订阅

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Confound Removal and Normalization in practice: A Neuroimaging Based Sex Prediction Case Study

Confound Removal and Normalization in Practice: A Neuroimagi...

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european conference on Machine Learning and principles and practice of knowledge discovery in databases (ECML PKDD)

作者： More, Shammi Eickhoff, Simon B. Caspers, Julian Patil, Kaustubh R. Forschungszentrum Julich Inst Neurosci & Med INM 7 Julich Germany Heinrich Heine Univ Dusseldorf Med Fac Inst Syst Neurosci Dusseldorf Germany Univ Hosp Dusseldorf Dept Diagnost & Intervent Radiol Dusseldorf Germany

ISBN: (纸本)9783030676704;9783030676698

Machine learning (ML) methods are increasingly being used to predict pathologies and biological traits using neuroimaging data. Here controlling for confounds is essential to get unbiased estimates of generalization performance and to identify the features driving predictions. However, a systematic evaluation of the advantages and disadvantages of available alternatives is lacking. this makes it difficult to compare results across studies and to build deployment quality models. Here, we evaluated two commonly used confound removal schemes-whole data confound regression (WDCR) and cross-validated confound regression (CVCR)-to understand their effectiveness and biases induced in generalization performance estimation. Additionally, we study the interaction of the confound removal schemes with Z-score normalization, a common practice in ML modelling. We applied eight combinations of confound removal schemes and normalization (pipelines) to decode sex from resting-state functional MRI (rfMRI) data while controlling for two confounds, brain size and age. We show that both schemes effectively remove linear univariate and multivariate confounding effects resulting in reduced model performance with CVCR providing better generalization estimates, i.e., closer to out-of-sample performance than WDCR. We found no effect of normalizing before or after confound removal. In the presence of dataset and confound shift, four tested confound removal procedures yielded mixed results, raising new questions. We conclude that CVCR is a better method to control for confounding effects in neuroimaging studies. We believe that our in-depth analyses shed light on choices associated with confound removal and hope that it generates more interest in this problem instrumental to numerous applications.

关键词： Confound removal Generalization Interpretability Sex classification Neuroimaging application

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Big Data Financial Sentiment Analysis in the european Bond Markets 1

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4th Workshop on MIning DAta for Financial ApplicationS (MIDAS) / european conference on Machine Learning and principles and practice of knowledge discovery in databases (ECML-PKDD)

作者： Pezzoli, Luca Tiozzo Consoli, Sergio Tosetti, Elisa JRC European Commiss Ispra Italy

ISBN: (数字)9783030377205

ISBN: (纸本)9783030377205;9783030377199

We exploit the novel Global Database of Events, Language and Tone (GDELT) to construct news-based financial sentiment measures capturing investor's opinions for three european countries, Italy, Spain and France. We study whether deterioration in investor's sentiment implies a rise in interest rates with respect to their German counterparts. Finally, we look at the link between agents' sentiment and their portfolio exposure on the Italian, French and Spanish markets.

关键词： Government yield spread Financial sentiment Random forest Quantile regression

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Monitoring the Business Cycle with Fine-Grained, Aspect-Based Sentiment Extraction from News 1

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4th Workshop on MIning DAta for Financial ApplicationS (MIDAS) / european conference on Machine Learning and principles and practice of knowledge discovery in databases (ECML-PKDD)

作者： Barbaglia, Luca Consoli, Sergio Manzan, Sebastiano European Commiss Joint Res Ctr Directorate A Strategy Work Programme & Resources Sci Dev Unit Via E Fermi 2749 I-21027 Ispra VA Italy

ISBN: (数字)9783030377205

ISBN: (纸本)9783030377205;9783030377199

We provide an overview on the development of a fine-grained, aspect-based sentiment analysis approach aimed at providing useful signals to improve forecasts of economic models and produce more accurate predictions. the approach is unsupervised since it relies on external lexical resources to associate a polarity score to a given term or concept. After providing an overview of the method under development, some preliminary findings are also given.

关键词： Economic forecasting and nowcasting Semantic reasoning Sentiment analysis News analysis

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4th International Workshop on New Frontiers in Mining Complex Patterns, NFMCP 2015 and Held in Conjunction with european conference on Machine Learning and principles and practice of knowledge discovery in databases, ECML-PKDD 2015

4th International Workshop on New Frontiers in Mining Comple...

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4th International Workshop on New Frontiers in Mining Complex Patterns, NFMCP 2015 and Held in Conjunction with european conference on Machine Learning and principles and practice of knowledge discovery in databases, ECML-PKDD 2015

ISBN: (纸本)9783319393148

the proceedings contain 15 papers. the special focus in this conference is on Data Stream Mining, Classification, Mining Complex Data and Sequences. the topics include: Adaptive ensembles for evolving data streams;combining block-based and online solutions;comparison of tree-based methods for multi-target regression on data streams;frequent itemsets mining in data streams using reconfigurable hardware;discovering and tracking organizational structures in event logs;intelligent adaptive ensembles for data stream mining;mining periodic changes in complex dynamic data through relational pattern discovery;the usefulness of roughly balanced bagging for complex and high-dimensional imbalanced data;classifying traces of event logs on the basis of security risks;redescription mining with multi-target predictive clustering trees;generalizing patterns for cross-domain analogy;spectral features for audio based vehicle identification;heterogeneous network decomposition and weighting with text mining heuristics;semi-supervised multivariate sequential pattern mining and evaluating a simple string representation for intra-day foreign exchange prediction.

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15th european conference on Machine Learning and principles and practice of knowledge discovery in databases, ECML PKDD 2016

15th European Conference on Machine Learning and Principles ...

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15th european conference on Machine Learning and principles and practice of knowledge discovery in databases, ECML PKDD 2016

ISBN: (纸本)9783319461274

the proceedings contain 50 papers. the special focus in this conference is on Machine Learning and knowledge discovery in databases. the topics include: An adaptive quasi-Newton algorithm for training RNNs;semi-supervised tensor factorization for brain network analysis;scalable hyperparameter optimization with products of Gaussian process experts;incremental commute time using random walks and online anomaly detection;online density estimation of heterogeneous data streams in higher dimensions;graphical model sketch;laplacian Hamiltonian Monte Carlo;functional bid landscape forecasting for display advertising;maximizing time-decaying influence in social networks;learning beyond predefined label space via Bayesian nonparametric topic modelling;efficient Bayesian maximum margin multiple kernel learning;transactional tree mining;Gaussian process pseudo-likelihood models for sequence labeling;online structure learning using background knowledge axiomatization;fast dense-block detection in tensors with quality guarantees;multi-objective group discovery on the social web;sequential data classification in the space of liquid state machines;semigeometric tiling of event sequences;attribute conjunction learning with recurrent neural network;aggregating crowdsourced ordinal labels via Bayesian clustering;joint learning of entity semantics and relation pattern for relation extraction;a bootstrapping approach for aligning heterogenous social networks;interactive learning from multiple noisy labels;structure pattern analysis and cascade prediction in social networks;pure exploration for max-quantile bandits;native advertisement selection and allocation in social media post feeds;asynchronous feature extraction for large-scale linear predictors and cost-aware early classification of time series.

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15th european conference on Machine Learning and principles and practice of knowledge discovery in databases, ECML PKDD 2016

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15th european conference on Machine Learning and principles and practice of knowledge discovery in databases, ECML PKDD 2016

ISBN: (纸本)9783319462264

the proceedings contain 50 papers. the special focus in this conference is on Machine Learning and knowledge discovery in databases. the topics include: AUC-Maximized deep convolutional neural fields for protein sequence labeling;a novel incremental covariance-guided one-class support vector machine;learning efficiently in semantic based regularization;persistent roles in online social networks;a split-merge DP-means algorithm to avoid local minima;efficient distributed decision trees for robust regression;fast hoeffding drift detection method for evolving data streams;differentially private user data perturbation with multi-level privacy controls;on dynamic feature weighting for feature drifting data streams;modeling sequential preferences with dynamic user and context factors;node re-ordering as a means of anomaly detection in time-evolving graphs;building ensembles of adaptive nested dichotomies with random-pair selection;credible review detection with limited information using consistency features;interactive visual data exploration with subjective feedback;coupled hierarchical dirichlet process mixtures for simultaneous clustering and topic modeling;enhancing traffic congestion estimation with social media by coupled hidden Markov model;learning distributed representations of users for source detection in online social networks;linear bandits in unknown environments;estimating labels from rough group comparisons;detecting public influence on news using topic-aware dynamic granger test;exploring a mixed representation for encoding temporal coherence;a tree-based subgoal discovery method to accelerate reinforcement learning;selecting collaborative filtering algorithms using metalearning and measuring the stability of feature selection.

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4th International Workshop on Mining Ubiquitous and Social Environments, MUSE 2013 in conjunction with the european conference on Machine Learning and principles and practice of knowledge discovery in databases, ECML-PKDD 2013

4th International Workshop on Mining Ubiquitous and Social E...

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4th International Workshop on Mining Ubiquitous and Social Environments, MUSE 2013 in conjunction with the european conference on Machine Learning and principles and practice of knowledge discovery in databases, ECML-PKDD 2013

ISBN: (纸本)9783319147222

the proceedings contain 10 papers. the special focus in this conference is on Problems related to the mining and Modeling, and recommendation in ubiquitous social media. the topics include: Exploratory subgroup analytics on ubiquitous data;finding needles in the haystacks;ontology-enabled access control and privacy recommendations;refining frequency-based tag reuse predictions by means of time and semantic context;modeling forgetting in a verbal and semantic tag recommender;utilizing online social network and location-based data to recommend products and categories in online marketplaces;open smartphone data for structured mobility and utilization analysis in ubiquitous systems and predictability analysis of aperiodic and periodic model for long-term human mobility using ambient sensors.

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european conference on Machine Learning and knowledge discovery in databases, ECML PKDD 2016

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15th european conference on Machine Learning and principles and practice of knowledge discovery in databases, ECML PKDD 2016

ISBN: (纸本)9783319461304

the proceedings contain 39 papers. the special focus in this conference is on Demo Track Contributions, Industrial Track Contributions and Nectar Track Contributions. the topics include: A tool for subjective and interactive visual data exploration;a web-based visualization tool for biclustering of multivariate time series;dynamic trafficmanagement using heterogeneous urban data;coordinate transformations for characterization and cluster analysis of spatial configurations in football;leveraging spatial abstraction in traffic analysis and forecasting with visual analytics;the SPMF open-source data mining library version 2;dynamic attributed network with community structure generator;real-time story tracking via social tags;learning language models from images with ReGLL;exploratory analysis of text collections through visualization and hybrid biclustering;pattern-based mining of satellite image time series;finding incident-related social media messages for emergency awareness;exploratory monitoring of twitter streams for the 2016 U.S. presidential election cycle;using social media to promote STEM education;a real time system for crowd flow prediction at metropolitan subway stations;finding dynamic co-evolving zones in spatial-temporal time series data;ECG monitoring in wearable devices by sparse models;intelligent urban data monitoring for smart cities;automatic detection of non-biological artifacts in ECGs acquired during cardiac computed tomography;active learning with rationales for identifying operationally significant anomalies in aviation;engine misfire detection with pervasive mobile audio;a KDD process for discrimination discovery;personality-based user modeling for music recommender systems and resource-aware steel production through data mining.

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Warped matrix factorisation for multi-view data integration 15th

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15th european conference on Machine Learning and principles and practice of knowledge discovery in databases, ECML PKDD 2016

作者： Pratanwanich, Naruemon Lió, Pietro Stegle, Oliver European Molecular Biology Laboratory European Bioinformatics Institute Wellcome Genome Campus Hinxton CambridgeCB10 1SD United Kingdom Open Targets Wellcome Genome Campus Hinxton CambridgeCB10 1SD United Kingdom Computer Laboratory University of Cambridge 15 JJ Thomson Avenue CambridgeCB3 0FD United Kingdom

ISBN: (纸本)9783319462264

Matrix factorisation is a widely used tool with applications in collaborative filtering, image analysis and in genomics. Several extensions of the classical model have been proposed, such as modelling of multiple related "data views" or accounting for side information on the latent factors. However, as the complexity of these models increases even subtle mismatches of the distributional assumptions on the input data can severely affect model performance. Here, we propose a simple yet effective solution to address this problem by modelling the observed data in a transformed or warped space. We derive a joint model of a multiview matrix factorisation model that infers view-specific data transformations and provide a computationally efficient variational approximation for parameter inference. We first validate the model on synthetic data before applying it to a matrix completion problem in genomics. We show that our model improves the imputation of missing values in genedisease association analysis and allows for discovering enhanced consensus structures across multiple data views the data and software related to this paper are available at https://***/PMBio/WarpedMF. © Springer International Publishing AG 2016.

关键词： Collaborative filtering

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the Difference and the Norm - Characterising Similarities and Differences Between databases

The Difference and the Norm - Characterising Similarities an...

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european conference on Machine Learning and principles and practice of knowledge discovery in databases (ECMLPKDD)

作者： Budhathoki, Kailash Vreeken, Jilles Max Planck Inst Informat D-66123 Saarbrucken Germany Univ Saarland D-66123 Saarbrucken Germany

ISBN: (纸本)9783319235257;9783319235240

Suppose we are given a set of databases, such as sales records over different branches. How can we characterise the differences and the norm between these datasets? that is, what are the patterns that characterise the general distribution, and what are those that are important to describe the individual datasets? We study how to discover these pattern sets simultaneously and without redundancy - automatically identifying those patterns that aid describing the overall distribution, as well as those pointing out those that are characteristic for specific databases. We define the problem in terms of the Minimum Description Length principle, and propose the DiffNorm algorithm to approximate the MDL-optimal summary directly from data. Empirical evaluation on synthetic and real-world data shows that DiffNorm efficiently discovers descriptions that accurately characterise the difference and the norm in easily understandable terms.

关键词： Database systems

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