Search Results for author: Guiping Hu

Found 10 papers, 1 papers with code

From WSI-level to Patch-level: Structure Prior Guided Binuclear Cell Fine-grained Detection

no code implementations26 Aug 2022 Baomin Wang, Geng Hu, Dan Chen, Lihua Hu, Cheng Li, Yu An, Guiping Hu, Guang Jia

The coarse detection network is a multi-task detection framework based on circular bounding boxes for cells detection, and central key points for nucleus detection.

whole slide images

A reinforcement learning approach to resource allocation in genomic selection

no code implementations22 Jul 2021 Saba Moeinizade, Guiping Hu, Lizhi Wang

Genomic selection (GS) is a technique that plant breeders use to select individuals to mate and produce new generations of species.

reinforcement-learning Reinforcement Learning +1

Corn Yield Prediction with Ensemble CNN-DNN

no code implementations29 May 2021 Mohsen Shahhosseini, Guiping Hu, Saeed Khaki, Sotirios V. Archontoulis

Two scenarios for ensemble creation are considered: homogenous and heterogeneous ensembles.

Management

Improved Weighted Random Forest for Classification Problems

no code implementations1 Sep 2020 Mohsen Shahhosseini, Guiping Hu

Several studies have shown that combining machine learning models in an appropriate way will introduce improvements in the individual predictions made by the base models.

BIG-bench Machine Learning Classification +4

A Hybrid Two-layer Feature Selection Method Using GeneticAlgorithm and Elastic Net

no code implementations30 Jan 2020 Fatemeh Amini, Guiping Hu

In the first layer of the proposed method, the Genetic Algorithm(GA) has been adopted as a wrapper to search for the optimal subset of predictors, which aims to reduce the number of predictors and the prediction error.

Computational Efficiency feature selection

Forecasting Corn Yield with Machine Learning Ensembles

no code implementations18 Jan 2020 Mohsen Shahhosseini, Guiping Hu, Sotirios V. Archontoulis

The emerge of new technologies to synthesize and analyze big data with high-performance computing, has increased our capacity to more accurately predict crop yields.

BIG-bench Machine Learning Feature Importance

Optimizing Ensemble Weights and Hyperparameters of Machine Learning Models for Regression Problems

1 code implementation14 Aug 2019 Mohsen Shahhosseini, Guiping Hu, Hieu Pham

To this end, an optimization-based nested algorithm that considers tuning hyperparameters as well as finding the optimal weights to combine ensembles (Generalized Weighted Ensemble with Internally Tuned Hyperparameters (GEM-ITH)) is designed.

BIG-bench Machine Learning regression

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