Cyril Goutte – författare

This book constitutes the refereed proceedings of the 33rd Canadian Conference on Artificial Intelligence, Canadian AI 2020, which was planned to take place in Ottawa, ON, Canada. Due to the COVID-19 pandemic, however, it was held virtually during May 13–15, 2020.The 31 regular papers and 24 short papers presented together with 4 Graduate Student Symposium papers were carefully reviewed and selected from a total of 175 submissions. The selected papers cover a wide range of topics, including machine learning, pattern recognition, natural language processing, knowledge representation, cognitive aspects of AI, ethics of AI, and other important aspects of AI research.

This book constitutes the refereed proceedings of the 33rd Canadian Conference on Artificial Intelligence, Canadian AI 2020, which was planned to take place in Ottawa, ON, Canada. Due to the COVID-19 pandemic, however, it was held virtually during May 13–15, 2020.The 31 regular papers and 24 short papers presented together with 4 Graduate Student Symposium papers were carefully reviewed and selected from a total of 175 submissions. The selected papers cover a wide range of topics, including machine learning, pattern recognition, natural language processing, knowledge representation, cognitive aspects of AI, ethics of AI, and other important aspects of AI research.

Technical Report from the year 1998 in the subject Mathematics - Statistics, grade: 1.0, Technical University of Denmark (Institute for Mathematical Modeling), language: English, abstract: Most human brain imaging experiments involve a number of subjects that is unusually low by accepted statistical standards. Although there are a number of practical reasons for using small samples in neuroimaging we need to face the question regarding whether results obtained with only a few subjects will generalise to a larger population. In this contribution we address this issue using a Bayesian framework, derive confidence intervals for small samples experiments, and discuss the issue of the prior.

Doctoral Thesis / Dissertation from the year 1997 in the subject Computer Sciences - Artificial Intelligence, grade: n/a, , language: English, abstract: Unravel the complexities of data-driven modeling and step into the realm where accuracy meets resilience in the face of uncertainty. This groundbreaking work delves deep into the heart of statistical learning and regularization, offering a transformative perspective on regression techniques. Discover how these powerful methods can revolutionize system identification and time series modeling, unlocking unprecedented levels of precision and reliability. Explore cutting-edge approaches to model parameter estimation, fortified by rigorous mathematical frameworks and innovative algorithms designed to combat overfitting and enhance generalization. Witness the evolution of model robustness as regularization techniques are meticulously applied to mitigate the impact of noisy data and spurious correlations. This research bridges the gap between theoretical advancements and practical applications, providing invaluable insights for researchers and practitioners alike. Whether you're grappling with the intricacies of dynamic systems or seeking to forecast future trends, this exploration of statistical learning, regularization, and regression will equip you with the tools and knowledge to conquer even the most challenging modeling tasks. Journey through the landscape of data analysis, guided by a comprehensive treatment of both established techniques and novel methodologies, and emerge with a profound understanding of how to build models that are not only accurate but also resilient and insightful. From the foundations of regression to the frontiers of system identification and time series analysis, this is your compass for navigating the ever-evolving world of data-driven decision-making. Prepare to challenge conventional wisdom, embrace the power of regularization, and unlock the true potential of your data. The exploration of statistical learning and its pivotal role in fortifying regression models within the realms of system identification and time series modeling will empower readers to construct models characterized by unparalleled precision and dependability. By navigating the nuances of parameter estimation and employing advanced regularization strategies, this thesis equips you to develop models robust enough to resist the detrimental impact of noisy data and misleading correlations. It is an essential resource for anyone committed to harnessing the full potential of data for informed decision-making and innovative solutions.