Results 31 to 40 of about 61,068 (254)

Perbandingan Metode Ensemble Learning pada Klasifikasi Penyakit Diabetes

Jurnal Masyarakat Informatika, 2022
Diabetes merupakan salah satu penyakit dalam dunia medis yang ditandai dengan kadar gula dalam darah yang tinggi pada penderitanya. Menurut data dari Organisasi Kesehatan Dunia (WHO), pada rentang tahun 1980 sampai 2014, terjadi peningkatan kasus ...
Linggar Maretva Cendani, Adi Wibowo
doaj   +1 more source

Historical Gradient Boosting Machine

EPiC Series in Computing, 2018
We introduce the Historical Gradient Boosting Machine with the objective of improving the convergence speed of gradient boosting. Our approach is analyzed from the perspective of numerical optimization in function space and considers gradients in previous steps, which have rarely been appreciated by traditional methods.
Zeyu Feng, Chang Xu 0002, Dacheng Tao
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Probabilistic Gradient Boosting Machines for Large-Scale Probabilistic Regression [PDF]

Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, 2021
Gradient Boosting Machines (GBM) are hugely popular for solving tabular data problems. However, practitioners are not only interested in point predictions, but also in probabilistic predictions in order to quantify the uncertainty of the predictions.
Olivier Sprangers, Sebastian Schelter, Maarten de Rijke   +2 more
openaire   +4 more sources

Assessing the compressive strength of self-compacting concrete with recycled aggregates from mix ratio using machine learning approach

Journal of Materials Research and Technology, 2023
The requirement of the construction sector pushes researchers and academicians to determine the 28-day concrete compressive strength due to less consumption of natural products and reduced cost.
P. Jagadesh, Jesús de Prado-Gil, Neemias Silva-Monteiro, Rebeca Martínez-García   +3 more
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Regionalization of hydrological model parameters using gradient boosting machine [PDF]

Hydrology and Earth System Sciences, 2021
Abstract. The regionalization of hydrological model parameters is key to hydrological predictions in ungauged basins. The commonly used multiple linear regression (MLR) method may not be applicable in complex and nonlinear relationships between model parameters and watershed properties.
Z. Song, Z. Song, J. Xia, J. Xia, J. Xia, G. Wang, G. Wang, G. Wang, D. She, D. She, C. Hu, C. Hu, S. Hong, S. Hong   +13 more
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Prediction of Emergency Cesarean Section Using Machine Learning Methods: Development and External Validation of a Nationwide Multicenter Dataset in Republic of Korea

Life, 2022
This study was a multicenter retrospective cohort study of term nulliparous women who underwent labor, and was conducted to develop an automated machine learning model for prediction of emergent cesarean section (CS) before onset of labor.
Jeong Ha Wie, Se Jin Lee, Sae Kyung Choi, Yun Sung Jo, Han Sung Hwang, Mi Hye Park, Yeon Hee Kim, Jae Eun Shin, Ki Cheol Kil, Su Mi Kim, Bong Suk Choi, Hanul Hong, Hyun-Joo Seol, Hye-Sung Won, Hyun Sun Ko, Sunghun Na   +15 more
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Design of Automatic Tool for Diagnosis of Pneumonia Using Boosting Techniques [PDF]

Brazilian Archives of Biology and Technology, 2022
Covid-19 is today's pandemic disease and can cause the hospital crowded. Additionally, It affects the lungs and may cause pneumonia. The most popular technique for diagnosis of pneumonia is the evaluation of X-ray.
Seda Postalcioglu
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HIBoosting: A Recommender System Based on a Gradient Boosting Machine [PDF]

IEEE Access, 2019
Based on explicit data, collaborative filtering is one of the most valuable technologies of a recommender system. However, the further development of a recommender system has been restricted to some extent by the problems of cold start and data sparsity. To weaken the effect caused by data loss, some implicit feedback data are typically introduced into
Yabin Shao, Chuanlong Wang
openaire   +2 more sources

Machine Learning-Based Forecasting of Bitcoin Price Movements

Proceedings of the International Conference on Applied Innovations in IT
In the volatile realm of cryptocurrency markets, this research explores the intricate dance of Bitcoin price dynamics through the lens of machine learning. Employing a multifaceted approach, we harness the power of Long Short-Term Memory (LSTM) networks,
Darko Angelovski, Bojana Velichkovska, Goran Jakimovski, Danijela Efnusheva, Marija Kalendar   +4 more
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Gradient boosting machine with partially randomized decision trees

CoRR, 2020
The gradient boosting machine is a powerful ensemble-based machine learning method for solving regression problems. However, one of the difficulties of its using is a possible discontinuity of the regression function, which arises when regions of training data are not densely covered by training points.
Andrei V. Konstantinov, Lev V. Utkin
openaire   +2 more sources