Results 51 to 60 of about 718,596 (262)

Topic Models Ensembles for AD-HOC Information Retrieval

Information, 2021
Ad hoc information retrieval (ad hoc IR) is a challenging task consisting of ranking text documents for bag-of-words (BOW) queries. Classic approaches based on query and document text vectors use term-weighting functions to rank the documents.
Pablo Ormeño, Marcelo Mendoza, Carlos Valle   +2 more
doaj   +1 more source

Engineering tandem VHHs to target different epitopes to enhance antibody‐dependent cell‐mediated cytotoxicity

FEBS Open Bio, EarlyView.
Tandem VHH targeting distinct EGFR epitopes were engineered into a monovalent bispecific antibody (7D12‐EGA1‐Fc) with more potent ADCC without increasing affinity to EGFR. Structural modeling of 7D12‐EGA1‐Fc showed cross‐linking of separate EGFR domains to enhance CD16a engagement on NK cells.
Yuqiang Xu, Hao Jiang, Limin Chen, Fulai Zhou, Ying Jin, Mark L. Chiu   +5 more
wiley   +1 more source

Hedging and Boosting Criticism in Dissertation Thesis Reviews

ELOPE
Dissertation thesis reviews within the Czech academic context are, unlike journal article peer reviews, non-blind, non-anonymous, publicly available texts. The key feature of these reviews is evaluation, and they require a substantial amount of facework
Magda Sučková, Petra Zmrzlá
doaj   +1 more source

RolexBoost: A Rotation-Based Boosting Algorithm With Adaptive Loss Functions

IEEE Access, 2020
We propose a new ensemble algorithm, called RolexBoost (Rotation-Flexible AdaBoost) that can not only secure diversity within an ensemble by rotating the feature axes in conjunction with performing the random subspace method for each bootstrap sample ...
Dong-Hyuk Yang, Hyeong-Jun Lee, Dong-Joon Lim   +2 more
doaj   +1 more source

Boosting with early stopping: Convergence and consistency

, 2005
Boosting is one of the most significant advances in machine learning for classification and regression. In its original and computationally flexible version, boosting seeks to minimize empirically a loss function in a greedy fashion.
Yu, Bin, Zhang, Tong
core   +1 more source

Intercompartmental communication in senescence

FEBS Open Bio, EarlyView.
Senescent cells experience structural changes in the plasma membrane, endoplasmic reticulum, mitochondria, lysosomes, nucleus, and cytoskeleton. These alterations disrupt crosstalk among cellular compartments, impairing vesicular trafficking, contact sites, and molecular flow.
Krystyna Mazan‐Mamczarz, Eleanor J. Wind, Jixiang Leng, Myriam Gorospe   +3 more
wiley   +1 more source

Diverse Models, United Goal: A Comprehensive Survey of Ensemble Learning

CAAI Transactions on Intelligence Technology
Ensemble learning, a pivotal branch of machine learning, amalgamates multiple base models to enhance the overarching performance of predictive models, capitalising on the diversity and collective wisdom of the ensemble to surpass individual models and ...
Ziwei Fan, Zhiwen Yu, Kaixiang Yang, Wuxing Chen, Xiaoqing Liu, Guojie Li, Xianling Yang, C. L. Philip Chen   +7 more
doaj   +1 more source

Exploiting the Regularized Greedy Forest Algorithm Through Active Learning for Predicting Student Grades: A Case Study

Knowledge
Student performance prediction is a critical research challenge in the field of educational data mining. To address this issue, various machine learning methods have been employed with significant success, including instance-based algorithms, decision ...
Maria Tsiakmaki, Georgios Kostopoulos, Sotiris Kotsiantis   +2 more
doaj   +1 more source

Natural Products as Geroprotective Modulators in Diabetic Nephropathy: A Mechanistic Framework Integrating Aging Hallmarks and the AMPK–SIRT1–Nrf2 Axis

Aging and Cancer, EarlyView.
Natural products target the aging kidney in diabetic nephropathy by restoring the AMPK–SIRT1–Nrf2 axis, reducing oxidative stress, inflammation, fibrosis, and cellular senescence while enhancing mitochondrial biogenesis and antioxidant defenses.
Sherif Hamidu, Latif Adams, Eunice Ampem‐Danso, Justice Kumi, Abigail Aning, Fatima Hamidu, Mustapha Abdul Mumin Mohammed, Ayuba Nii Darku Ankrah, Seth Kwabena Amponsah   +8 more
wiley   +1 more source

$L_2$ boosting in kernel regression

, 2009
In this paper, we investigate the theoretical and empirical properties of $L_2$ boosting with kernel regression estimates as weak learners. We show that each step of $L_2$ boosting reduces the bias of the estimate by two orders of magnitude, while it ...
Ha, S., Lee, Y. K., Park, B. U.
core   +2 more sources