Results 71 to 80 of about 1,243,089 (305)

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

Comment: Boosting Algorithms: Regularization, Prediction and Model Fitting

, 2008
The authors are doing the readers of Statistical Science a true service with a well-written and up-to-date overview of boosting that originated with the seminal algorithms of Freund and Schapire. Equally, we are grateful for high-level software that will
Buja, Andreas, Mease, David, Wyner, Abraham J.   +2 more
core   +1 more source

TREX1, a predator for treating MSI‐H tumors?

Molecular Oncology, EarlyView.
Immunotherapy benefits many patients; yet, some with MSI‐H tumors remain unresponsive despite their high immunogenicity. Xu et al. reveal that TREX1 enables immune evasion by degrading cytosolic DNA and suppressing cGAS–STING–IFN‐I signaling. TREX1 loss restores DNA sensing, increases CD8+ T and NK cell infiltration, and boosts antitumor immunity ...
Elena Benidovskaya, Joséphine Deneft, Marc Van den Eynde   +2 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

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

Developing evidence‐based, cost‐effective P4 cancer medicine for driving innovation in prevention, therapeutics, patient care and reducing healthcare inequalities

Molecular Oncology, EarlyView.
The cancer problem is increasing globally with projections up to the year 2050 showing unfavourable outcomes in terms of incidence and cancer‐related deaths. The main challenges are prevention, improved therapeutics resulting in increased cure rates and enhanced health‐related quality of life.
Ulrik Ringborg, Joachim von Braun, Julio Celis, Anton Berns, Michael Baumann, Tit Albreht, Nancy Abou‐Zeid, Vanderlei Bagnato, Christian Brandts, Chien‐Jen Chen, Massimiliano di Pietro, Manjit Dosanjh, Thomas Dubois, Alexander Eggermont, Angelika Eggert, Ingemar Ernberg, Sara Faithfull, Johannes Förner, Stefan Fröhling, Manuel Heitor, Leroy Hood, Wei Jiang, Bengt Jönsson, Ravi Kannan, Maria Leptin, Su Li, Peter Lindgren, Douglas Lowy, Jun Ma, Alex Markham, Péter Nagy, Simon Oberst, M. Iqbal Parker, Danielle Rodin, Kevin Ryan, Joachim Schüz, Richard Sullivan, Josep Tabernero, Peter Turkson, Oliver Várhelyi, Harold Varmus, Chijie Wang, Elisabete Weiderpass, Nils Wilking   +43 more
wiley   +1 more source

Targeting p38α in cancer: challenges, opportunities, and emerging strategies

Molecular Oncology, EarlyView.
p38α normally regulates cellular stress responses and homeostasis and suppresses malignant transformation. In cancer, however, p38α is co‐opted to drive context‐dependent proliferation and dissemination. p38α also supports key functions in cells of the tumor microenvironment, including fibroblasts, myeloid cells, and T lymphocytes.
Angel R. Nebreda
wiley   +1 more source

Optimization by gradient boosting

, 2017
Gradient boosting is a state-of-the-art prediction technique that sequentially produces a model in the form of linear combinations of simple predictors---typically decision trees---by solving an infinite-dimensional convex optimization problem.
Biau, Gérard, Cadre, Benoît
core   +2 more sources

RaMBat: Accurate identification of medulloblastoma subtypes from diverse data sources with severe batch effects

Molecular Oncology, EarlyView.
To integrate multiple transcriptomics data with severe batch effects for identifying MB subtypes, we developed a novel and accurate computational method named RaMBat, which leveraged subtype‐specific gene expression ranking information instead of absolute gene expression levels to address batch effects of diverse data sources.
Mengtao Sun, Jieqiong Wang, Shibiao Wan
wiley   +1 more source