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Neural Connectomics Challenge
This book illustrates the thrust of the scientific community to use machine learning concepts for tackling a complex problem: given time series of neuronal spontaneous activity, which is the underlying connectivity between the neurons in the network? The contributing authors also develop tools for the advancement of neuroscience through machine learning techniques, with a focus on the major open problems in neuroscience. While the techniques have been developed for a specific application, they address the more general problem of network reconstruction from observational time series, a problem of interest in a wide variety of domains, including econometrics, epidemiology, and climatology, to cite only a few. divThe book is designed for the mathematics, physics and computer science communities that carry out research in neuroscience problems. The content is also suitable for the machine learning community because it exemplifies how to approach the same problem from different perspectives./divdivbr/divdivbr
Advanced Analytics and Learning on Temporal Data
This book constitutes the refereed proceedings of the 4th ECML PKDD Workshop on Advanced Analytics and Learning on Temporal Data, AALTD 2019, held in Würzburg, Germany, in September 2019. The 7 full papers presented together with 9 poster papers were carefully reviewed and selected from 31 submissions. The papers cover topics such as temporal data clustering; classification of univariate and multivariate time series; early classification of temporal data; deep learning and learning representations for temporal data; modeling temporal dependencies; advanced forecasting and prediction models; space-temporal statistical analysis; functional data analysis methods; temporal data streams; interpretable time-series analysis methods; dimensionality reduction, sparsity, algorithmic complexity and big data challenge; and bio-informatics, medical, energy consumption, on temporal data.
Statistical Models for Data Analysis
The papers in this book cover issues related to the development of novel statistical models for the analysis of data. They offer solutions for relevant problems in statistical data analysis and contain the explicit derivation of the proposed models as well as their implementation. The book assembles the selected and refereed proceedings of the biannual conference of the Italian Classification and Data Analysis Group (CLADAG), a section of the Italian Statistical Society.
Analysis of Large and Complex Data
This book offers a snapshot of the state-of-the-art in classification at the interface between statistics, computer science and application fields. The contributions span a broad spectrum, from theoretical developments to practical applications; they all share a strong computational component. The topics addressed are from the following fields: Statistics and Data Analysis; Machine Learning and Knowledge Discovery; Data Analysis in Marketing; Data Analysis in Finance and Economics; Data Analysis in Medicine and the Life Sciences; Data Analysis in the Social, Behavioural, and Health Care Sciences; Data Analysis in Interdisciplinary Domains; Classification and Subject Indexing in Library and Information Science. The book presents selected papers from the Second European Conference on Data Analysis, held at Jacobs University Bremen in July 2014. This conference unites diverse researchers in the pursuit of a common topic, creating truly unique synergies in the process.
Advanced Analytics and Learning on Temporal Data
This volume LNCS 14343 constitutes the refereed proceedings of the 8th ECML PKDD Workshop, AALTD 2023, in Turin, Italy, in September 2023. The 20 full papers were carefully reviewed and selected from 28 submissions. They are organized in the following topical section as follows: Machine Learning; Data Mining; Pattern Analysis; Statistics to Share their Challenges and Advances in Temporal Data Analysis.
Data Analysis, Machine Learning and Applications
Data analysis and machine learning are research areas at the intersection of computer science, artificial intelligence, mathematics and statistics. They cover general methods and techniques that can be applied to a vast set of applications such as web and text mining, marketing, medical science, bioinformatics and business intelligence. This volume contains the revised versions of selected papers in the field of data analysis, machine learning and applications presented during the 31st Annual Conference of the German Classification Society (Gesellschaft für Klassifikation - GfKl). The conference was held at the Albert-Ludwigs-University in Freiburg, Germany, in March 2007.
Advanced Analytics and Learning on Temporal Data
This book constitutes the refereed proceedings of the 6th ECML PKDD Workshop on Advanced Analytics and Learning on Temporal Data, AALTD 2021, held during September 13-17, 2021. The workshop was planned to take place in Bilbao, Spain, but was held virtually due to the COVID-19 pandemic. The 12 full papers presented in this book were carefully reviewed and selected from 21 submissions. They focus on the following topics: Temporal Data Clustering; Classification of Univariate and Multivariate Time Series; Multivariate Time Series Co-clustering; Efficient Event Detection; Modeling Temporal Dependencies; Advanced Forecasting and Prediction Models; Cluster-based Forecasting; Explanation Methods for Time Series Classification; Multimodal Meta-Learning for Time Series Regression; and Multivariate Time Series Anomaly Detection.
Theory and Applications of Time Series Analysis and Forecasting
This book presents a selection of peer-reviewed contributions on the latest developments in time series analysis and forecasting, presented at the 7th International Conference on Time Series and Forecasting, ITISE 2021, held in Gran Canaria, Spain, July 19-21, 2021. It is divided into four parts. The first part addresses general modern methods and theoretical aspects of time series analysis and forecasting, while the remaining three parts focus on forecasting methods in econometrics, time series forecasting and prediction, and numerous other real-world applications. Covering a broad range of topics, the book will give readers a modern perspective on the subject. The ITISE conference series provides a forum for scientists, engineers, educators and students to discuss the latest advances and implementations in the foundations, theory, models and applications of time series analysis and forecasting. It focuses on interdisciplinary research encompassing computer science, mathematics, statistics and econometrics.
A Holistic, Decision-Theoretic Framework for Pool-Based Active Learning
Efficient labeling is an important topic in machine learning research as classifiers need labeled data. Whereas unlabeled data is easily gathered, labeling is exhausting, time-consuming, or expensive and should, therefore, be reduced to a minimum. Active learning aims to actively select useful, unlabeled instances for label acquisition to reduce the labeling effort while providing labeled training data such that the classifier performs well. This thesis proposes Probabilistic Active Learning, a holistic, decision-theoretic framework for active learning that enables optimization for every performance measure and classifier. Using the holistic mathematical description, we can define an upper b...
Advances in Machine Learning
The First Asian Conference on Machine Learning (ACML 2009) was held at Nanjing, China during November 2–4, 2009.This was the ?rst edition of a series of annual conferences which aim to provide a leading international forum for researchers in machine learning and related ?elds to share their new ideas and research ?ndings. This year we received 113 submissions from 18 countries and regions in Asia, Australasia, Europe and North America. The submissions went through a r- orous double-blind reviewing process. Most submissions received four reviews, a few submissions received ?ve reviews, while only several submissions received three reviews. Each submission was handled by an Area Chair who co...
