Results 101 to 110 of about 24,126 (306)

Application of Non-Sparse Manifold Regularized Multiple Kernel Classifier

Mathematics
Non-sparse multiple kernel learning is efficient but not directly able to be applied in a semi-supervised scenario; therefore, we extend it to semi-supervised learning by using a manifold regularization.
Tao Yang
doaj   +1 more source

Matchings in regular graphs

Discrete Mathematics, 1981
AbstractWe consider k-regular graphs with specified edge connectivity and show how some classical theorems and some new results concerning the existence of matchings in such graphs can be proved by using the polyhedral characterization of Edmonds.
Denis Naddef, William R. Pulleyblank
openaire   +1 more source

People Counting and Positioning Using Low‐Resolution Infrared Images for FeFET‐Based In‐Memory Computing

Advanced Electronic Materials, EarlyView.
In this work, low‐resolution infrared imaging is combined with a 28 nm FeFET IMC architecture to enable compact, energy‐efficient edge inference. MLC FeFET devices are experimentally characterized, and controlled multi‐level current accumulation is validated at crossbar array level.
Alptekin Vardar, Li Zhang, Alexander Rehmer, Marc Lupp, Subhashree Baskaran, Osama Abdelaal, Franz Müller, Nandakishor Yadav, Bing Li, Thomas Kämpfe   +9 more
wiley   +1 more source

Prediction models with graph kernel regularization for network data

, 2022
Traditional regression methods typically consider only covariate information and assume that the observations are mutually independent samples. However, samples usually come from individuals connected by a network in many modern applications.
Haojie Chen (6941959), Jie Liu (15128), Yang Yang (45629)   +2 more
core   +1 more source

Domain-Invariant Label Propagation With Adaptive Graph Regularization

IEEE Access
As an effective machine learning paradigm, domain adaptation (DA) learning aims to enhance the learning performance of the target domain by utilizing other relevant but distinct domain(s) (referred to as the source domain(s)).
Yanning Zhang, Jianwen Tao, Liangda Yan
doaj   +1 more source

Joint Nonnegative Matrix Factorization Based on Sparse and Graph Laplacian Regularization for Clustering and Co-Differential Expression Genes Analysis

Complexity, 2020
The explosion of multiomics data poses new challenges to existing data mining methods. Joint analysis of multiomics data can make the best of the complementary information that is provided by different types of data.
Ling-Yun Dai, Rong Zhu, Juan Wang
doaj   +1 more source

On the Role of Preprocessing and Memristor Dynamics in Reservoir Computing for Image Classification

Advanced Electronic Materials, EarlyView.
ABSTRACT Reservoir computing (RC) is an emerging recurrent neural network architecture that has attracted growing attention for its low training cost and modest hardware requirements. Memristor‐based circuits are particularly promising for RC, as their intrinsic dynamics can reduce network size and parameter overhead in tasks such as time‐series ...
Rishona Daniels, Duna Wattad, Ronny Ronen, David Saad, Shahar Kvatinsky   +4 more
wiley   +1 more source

Comparing parameter choice methods for regularization of ill-posed problems

, 2011
In the literature on regularization, many different parameter choice methods have been proposed in both deterministic and stochastic settings. However, based on the available information, it is not always easy to know how well a particular method will ...
Bauer, F., Lukas, M.A.
core  

Prediction of Structural Stability of Layered Oxide Cathode Materials: Combination of Machine Learning and Ab Initio Thermodynamics

Advanced Energy Materials, EarlyView.
In this work, we developed a phase‐stability predictor by combining machine learning and ab initio thermodynamics approaches, and identified the key factors determining the favorable phase for a given composition. Specifically, a lower TM ionic potential, higher Na content, and higher mixing entropy favor the O3 phase.
Liang‐Ting Wu, Milica Zdravković, Dijana Milosavljević, Konstantin Köster, Olivier Guillon, Jyh‐Chiang Jiang, Payam Kaghazchi   +6 more
wiley   +1 more source

Unsupervised vocabulary discovery using non-negative matrix factorizationwith graph regularization

, 2011
In this paper, we present a model for unsupervised pattern discovery using non-negative matrix factorization (NMF) with graph regularization. Though the regularization can be applied to many applications, we illustrate its effectiveness in a task of ...
Sun, Meng, Hugo Van hamme, Van hamme, Hugo, Meng Sun   +3 more
core   +1 more source