Results 21 to 30 of about 823,510 (266)

Incremental Learning-to-Learn with Statistical Guarantees [PDF]

CoRR, 2018
In learning-to-learn the goal is to infer a learning algorithm that works well on a class of tasks sampled from an unknown meta distribution. In contrast to previous work on batch learning-to-learn, we consider a scenario where tasks are presented sequentially and the algorithm needs to adapt incrementally to improve its performance on future tasks ...
Giulia Denevi, Carlo Ciliberto, Dimitris Stamos, Massimiliano Pontil   +3 more
openaire   +2 more sources

Active Learning with Statistical Models. [PDF]

Journal of Artificial Intelligence Research, 1995
For many types of machine learning algorithms, one can compute the statistically `optimal' way to select training data. In this paper, we review how optimal data selection techniques have been used with feedforward neural networks. We then show how the same principles may be used to select data for two alternative, statistically-based ...
David A. Cohn, Zoubin Ghahramani, Michael I. Jordan   +2 more
openaire   +5 more sources

Statistical learning in infants [PDF]

Proceedings of the National Academy of Sciences, 2002
Statistical learning has become the subject of some considerable debate within cognitive psychology (1, 2). The debate reached particular prominence with the advent of sophisticated computational models of such learning based on neurally inspired notions of spreading activation within highly distributed systems of interacting units, so-called neural ...
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Multi-decadal streamflow projections for catchments in Brazil based on CMIP6 multi-model simulations and neural network embeddings for linear regression models [PDF]

Hydrology and Earth System Sciences
A linear regression model is developed to link anomalies of streamflow to anomalies of precipitation amounts and temperature with the goal of making multi-decadal streamflow projections based on CMIP6 multi-model simulations.
M. Scheuerer, E. Byermoen, E. Byermoen, J. Ribeiro de Oliveira, T. Roksvåg, D. Vikhamar Schuler   +5 more
doaj   +1 more source

Multivariate Adaptive Regression Splines Enhance Genomic Prediction of Non-Additive Traits

Agronomy
The present work used Multivariate Adaptive Regression Splines (MARS) for genomic prediction and to study the non-additive fraction present in a trait. To this end, 12 scenarios for an F2 population were simulated by combining three levels of broad-sense
Maurício de Oliveira Celeri, Weverton Gomes da Costa, Ana Carolina Campana Nascimento, Camila Ferreira Azevedo, Cosme Damião Cruz, Vitor Seiti Sagae, Moysés Nascimento   +6 more
doaj   +1 more source

Unlocking stroke prediction: Harnessing projection-based statistical feature extraction with ML algorithms

Heliyon
Non-communicable diseases, such as cardiovascular disease, cancer, chronic respiratory diseases, and diabetes, are responsible for approximately 71% of all deaths worldwide.
Saad Sahriar, Sanjida Akther, Jannatul Mauya, Ruhul Amin, Md Shahajada Mia, Sabba Ruhi, Md Shamim Reza   +6 more
doaj   +1 more source

Optional Defaultable Markets

Risks, 2017
The paper deals with defaultable markets, one of the main research areas of mathematical finance. It proposes a new approach to the theory of such markets using techniques from the calculus of optional stochastic processes on unusual probability spaces ...
Mohamed N. Abdelghani, Alexander V. Melnikov   +1 more
doaj   +1 more source

Statistical Relational Learning [PDF]

, 2013
Relational learning refers to learning from data that have a complex structure. This structure may be either internal (a data instance may itself have a complex structure) or external (relationships between this instance and other data elements). Statistical relational learning refers to the use of statistical learning methods in a relational learning ...
openaire   +2 more sources

Missing Risk Factor Prediction in Cardiovascular Disease Using a Blended Dataset and Optimizing Classification With a Stacking Algorithm

Engineering Reports
Machine learning is important in the treatment of heart disease because it is capable of analyzing large amounts of patient data, such as medical records, imaging tests, and genetic information, in order to identify patterns and predict the risk of ...
Jannatul Mauya, Saad Sahriar, Sanjida Akther, Ruhul Amin, Sabba Ruhi, Md. Shamim Reza   +5 more
doaj   +1 more source

AI-accelerated protein-ligand docking for SARS-CoV-2 is 100-fold faster with no significant change in detection

Scientific Reports, 2023
Protein-ligand docking is a computational method for identifying drug leads. The method is capable of narrowing a vast library of compounds down to a tractable size for downstream simulation or experimental testing and is widely used in drug discovery ...
Austin Clyde, Xuefeng Liu, Thomas Brettin, Hyunseung Yoo, Alexander Partin, Yadu Babuji, Ben Blaiszik, Jamaludin Mohd-Yusof, Andre Merzky, Matteo Turilli, Shantenu Jha, Arvind Ramanathan, Rick Stevens   +12 more
doaj   +1 more source