Results 81 to 90 of about 379,537 (253)

Sparse Bayesian nonparametric regression [PDF]

Proceedings of the 25th international conference on Machine learning - ICML '08, 2008
One of the most common problems in machine learning and statistics consists of estimating the mean response Xβ from a vector of observations y assuming y = Xβ + e where X is known, β is a vector of parameters of interest and e a vector of stochastic errors.
Caron, F, Doucet, A
openaire   +2 more sources

A Smart Bio‐Battery Facilitates Diabetic Bone Defect Repair Via Inducing Macrophage Reprogramming and Synergistically Modulating Bone Remodeling Coupling

Advanced Functional Materials, EarlyView.
This research presents a novel implantable bio‐battery, GF‐OsG, tailored for diabetic bone repair. GF‐OsG generates microcurrents in high‐glucose conditions to enhance vascularization, shift macrophages to the M2 phenotype, and regulate immune responses.
Nanning Lv, Haifu Sun, Wenxiang Tang, Yonggang Li, Zhonglai Qian, Lihui Hong, Chong Chen, Hongye Li, Jiaxiang Bai, Yusen Qiao, Mingming Liu   +10 more
wiley   +1 more source

Sparse Additive Models

, 2008
We present a new class of methods for high-dimensional nonparametric regression and classification called sparse additive models (SpAM). Our methods combine ideas from sparse linear modeling and additive nonparametric regression.
Lafferty, John, Liu, Han, Ravikumar, Pradeep, Wasserman, Larry   +3 more
core   +2 more sources

Spectrally Tunable 2D Material‐Based Infrared Photodetectors for Intelligent Optoelectronics

Advanced Functional Materials, EarlyView.
Intelligent optoelectronics through spectral engineering of 2D material‐based infrared photodetectors. Abstract The evolution of intelligent optoelectronic systems is driven by artificial intelligence (AI). However, their practical realization hinges on the ability to dynamically capture and process optical signals across a broad infrared (IR) spectrum.
Junheon Ha, Yingshan Ma, Yong Nam An, Sung‐Un An, Hyeon Hak Jung, Suvi‐Tuuli Varjamo, Jiwon Yoo, Junho Min, Hanvit Kim, Faisal Ahmed, Sang Hoon Chae, Young Min Song, Weiwei Cai, Tawfique Hasan, Zhipei Sun, Dong‐Ho Kang, Hyeon‐Jin Shin, Yunyun Dai, Hoon Hahn Yoon   +18 more
wiley   +1 more source

Sparse Linear Discriminant Analysis With Constant Between-Class Distance for Feature Selection

IEEE Access
Feature selection is an important preprocessing step in machine learning to remove irrelevant and redundant features. Due to its ability to effectively maintain the discriminability of extracted features, Trace Ratio Linear Discriminant Analysis (TR-LDA)
Shuangle Guo, Yongxia Li, Jianguang Zhang, Yue Liu, Tian Tian, Mengchen Guo   +5 more
doaj   +1 more source

An Ultrafast Self‐Gelling Versatile Hydrogel for Rapid Infected Burn Wound Repair in Military Medicine

Advanced Functional Materials, EarlyView.
A self‐gelling PG@PAC (POD/Gel‐CDH@PA/CHX) powder is developed for infected burn care in austere settings. Upon contact with wound exudate, it instantly forms an adhesive hydrogel, providing simultaneous hemostasis, broad‐spectrum antibacterial activity, reactive oxygen species scavenging, and immunomodulation. In a murine model of S.
Liping Zhang, Zhangrui Wu, Xiansheng Zhao, Geng Wang, Quyang Yang, Minxia zhang, Xiaowei Wang, Xiujun Cheng, Changyue Wu, Dan Wu, Yutian Yang, Canbin Dong, Tao Zhang, Weiqiang Tan, Ningwen Zhu   +14 more
wiley   +1 more source

Incremental Sparse Density-Weighted Twin Support Vector Regression [PDF]

Jisuanji gongcheng
The Density-Weighted Twin Support Vector Regression(DWTSVR) is a regression algorithm that reflects the internal distribution of data with high prediction accuracy and robustness.
Weijie DING, Binjie GU, Feng PAN
doaj   +1 more source

Sparse Logistic Regression for RR Lyrae versus Binaries Classification

The Astrophysical Journal, 2023
RR Lyrae (RRL) stars are old, low-mass, radially pulsating variable stars in their core helium burning phase. They are popular stellar tracers and primary distance indicators since they obey well-defined period–luminosity relations in the near-infrared ...
Piero Trevisan, Mario Pasquato, Gaia Carenini, Nicolas Mekhaël, Vittorio F. Braga, Giuseppe Bono, Mohamad Abbas   +6 more
doaj   +1 more source

From Mechanics to Electronics: Influence of ALD Interlayers on the Multiaxial Electro‐Mechanical Behavior of Metal–Oxide Bilayers

Advanced Functional Materials, EarlyView.
Ultrathin AlOxHy interlayers between aluminum films and polymer substrates significantly improve electro‐mechanical properties of flexible thin film systems. By precisely controlling interlayer thickness using atomic layer deposition, this study identifies an optimal interlayer thickness of 5–10 nm that enhances ductility and delays cracking.
Johanna Byloff, Vivek Devulapalli, Daniele Casari, Thomas E. J. Edwards, Claus O. W. Trost, Megan J. Cordill, Shuhel Altaf Husain, Pierre‐Olivier Renault, Damien Faurie, Barbara Putz   +9 more
wiley   +1 more source

Selection of tuning parameters in bridge regression models via Bayesian information criterion

, 2012
We consider the bridge linear regression modeling, which can produce a sparse or non-sparse model. A crucial point in the model building process is the selection of adjusted parameters including a regularization parameter and a tuning parameter in bridge
A Antoniadis, AE Hoerl, C Park, C-H Zhang, CM Hurvich, CM Hurvich, G McDonald, G Schwarz, H Zou, H Zou, J Fan, J Fan, J Huang, J Huang, J Lv, JE Frank, K Knight, L Tierney, M Ishiguro, M Yuan, N Sugiura, P Bühlmann, P Craven, PHC Eilers, R Tibshirani, S Konishi, Shuichi Kawano, T Shimamura, WJ Fu, Y Liu   +29 more
core   +1 more source