Results 61 to 70 of about 451,604 (277)

Incremental multi-step Q-learning [PDF]

Machine Learning, 1994
This paper presents a novel incremental algorithm that combines Q-learning, a well-known dynamic programming-based reinforcement learning method, with the TD(A) return estimation process, which is typically used in actor-critic learning, another well-known dynamic programming-based reinforcement learning method.
Jing Peng, Ronald J. Williams
openaire   +2 more sources

Additive Gaussian Process Regression for Predictive Design of High‐Performance, Printable Silicones

Advanced Engineering Materials, EarlyView.
A chemistry‐aware design framework for tuning printable polydimethylsiloxane (PDMS) for vat photopolymerization (VPP) is developed using additive Gaussian process (GP) modeling. Polymer network mechanics informs variable groupings, feasible formulation constraints, and interaction variables.
Roxana Carbonell, Martha Leach, Hongtao Song, Carolyn Seepersad   +3 more
wiley   +1 more source

Novel Four-Layer Neural Network and Its Incremental Learning Based on Randomly Mapped Features

Jisuanji kexue yu tansuo, 2021
This paper proposes a four-layer neural network based on randomly feature mapping (FRMFNN) and its fast incremental learning algorithms. First, FRMFNN transforms the original input features into randomly mapped features by certain randomly mapping ...
YANG Yue, WANG Shitong
doaj   +1 more source

The Extreme Value Machine

, 2017
It is often desirable to be able to recognize when inputs to a recognition function learned in a supervised manner correspond to classes unseen at training time.
Boult, Terrance E., Jain, Lalit P., Rudd, Ethan M., Scheirer, Walter J.   +3 more
core   +1 more source

Numerical Modeling of Tank Cars Carrying Hazardous Materials With and Without Composite Metal Foam

Advanced Engineering Materials, EarlyView.
Large‐scale puncture models consisting of hazardous materials (HAZMATs) tank car with protective steel–steel composite metal foam (S–S CMF) are solved numerically. Tank car plate with added 10.91–13.33 mm thick S–S CMF layer does not puncture. Protective S–S CMF absorbs impact energy, reduces plate deformation, and prevents shear bands formation ...
Aman Kaushik, Afsaneh Rabiei
wiley   +1 more source

An Experimental Survey of Incremental Transfer Learning for Multicenter Collaboration

IEEE Access
Due to data privacy constraints, data sharing among multiple clinical centers is restricted, which impedes the development of high performance deep learning models from multicenter collaboration.
Yixing Huang, Christoph Bert, Ahmed Gomaa, Rainer Fietkau, Andreas Maier, Florian Putz   +5 more
doaj   +1 more source

ILAPF: Incremental Learning Assisted Particle Filtering

, 2018
This paper is concerned with dynamic system state estimation based on a series of noisy measurement with the presence of outliers. An incremental learning assisted particle filtering (ILAPF) method is presented, which can learn the value range of ...
Liu, Bin
core   +1 more source

Fostering Innovation: Streamlining Magnetocaloric Materials Research by Digitalization

Advanced Engineering Materials, EarlyView.
Magnetocaloric cooling (MCE) is an environmentally friendly refrigeration method with great potential. Optimizing MCE materials involves the preparation and screening of large quantities of samples, which in turn generates a large amount of data. A digitalization approach is presented that uses ontologies, knowledge graphs, and digital workflows to ...
Simon Bekemeier, Moritz Blum, Luana Caron, Alisa Chirkova, Philipp Cimiano, Basil Ell, Inga Ennen, Michael Feige, Maik Gaerner, Thomas Hilbig, Andreas Hütten, Günter Reiss, Tapas Samanta, Sonja Schöning, Christian Schröder, Lennart Schwan, Chris Taake, Martin Wortmann   +17 more
wiley   +1 more source

Fast Incremental SVDD Learning Algorithm with the Gaussian Kernel

, 2018
Support vector data description (SVDD) is a machine learning technique that is used for single-class classification and outlier detection. The idea of SVDD is to find a set of support vectors that defines a boundary around data.
Chaudhuri, Arin, Hu, Wenhao, Jiang, Hansi, Kakde, Deovrat, Wang, Haoyu   +4 more
core   +1 more source

Incremental Learning from Noisy Data [PDF]

Machine Learning, 1986
Induction of a concept description given noisy instances is difficult and is further exacerbated when the concepts may change over time. This paper presents a solution which has been guided by psychological and mathematical results. The method is based on a distributed concept description which is composed of a set of weighted, symbolic ...
Jeffrey C. Schlimmer, Richard H. Granger
openaire   +2 more sources