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检索条件"主题词=adaboost algorithm"

共 297 条记录，以下是111-120 订阅

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A Combination Forecasting Model Based on adaboost_GRNN in Depth- Averaged Currents Using Underwater Gliders

A Combination Forecasting Model Based on AdaBoost_GRNN in De...

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Global OCEANS Singapore - U.S. Gulf Coast Conference

作者： Zhang, Runfeng Wang, Yanhui Yang, Shaoqiong Wang, Shuxin Ma, Wei Tianjin Univ TJU Sch Mech Engn Tianjin 300350 Peoples R China Pilot Natl Lab Marine Sci & Technol Qingdao Qingdao 266237 Shandong Peoples R China

ISBN: (纸本)9781728154466

In this paper, a novel combination method based on the adaboost algorithm and the general regression neural network (GRNN) is proposed to predict the depth-averaged current (DAC) for the next profile using the underwater gliders (UGs). Firstly, considering changes of the seawater density and the hull deformation, the velocity calculation model of UGs and the calculation method of DAC are given. Secondly, two DAC forecasting models are established with historical data using the support vector machine (SVM) and the back-propagation neural network (BPNN) methods, respectively. Then, the forecasting models are combined nonlinearly by adopting the GRNN. Finally, outputs from the GRNN are combined using the adaboost algorithm, which further taken for the DAC forecasting. A sea trial was conducted using Petrel-II glider in the South China Sea, which verified the accuracy of the three forecasting models. Results demonstrate that the proposed combination method has a better performance in DAC forecasting than the other two single forecasting models.

关键词： Underwater glider adaboost algorithm depth-averaged current combination forecasting model

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adaboost-LSTM Ensemble Learning for Financial Time Series Forecasting 18th

AdaBoost-LSTM Ensemble Learning for Financial Time Series Fo...

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18th International Conference on Computational Science (ICCS)

作者： Sun, Shaolong Wei, Yunjie Wang, Shouyang Chinese Acad Sci Acad Math & Syst Sci Beijing Peoples R China Univ Chinese Acad Sci Sch Econ & Management Beijing Peoples R China Chinese Acad Sci Ctr Forecasting Sci Beijing Peoples R China

ISBN: (纸本)9783319937137;9783319937120

A hybrid ensemble learning approach is proposed to forecast financial time series combining adaboost algorithm and Long Short-Term Memory (LSTM) network. Firstly, by using adaboost algorithm the database is trained to get the training samples. Secondly, the LSTM is utilized to forecast each training sample separately. Thirdly, adaboost algorithm is used to integrate the forecasting results of all the LSTM predictors to generate the ensemble results. Two major daily exchange rate datasets and two stock market index datasets are selected for model evaluation and comparison. The empirical results demonstrate that the proposed adaboost-LSTM ensemble learning approach outperforms some other single forecasting models and ensemble learning approaches. This suggests that the adaboost-LSTM ensemble learning approach is a highly promising approach for financial time series data forecasting, especially for the time series data with nonlinearity and irregularity, such as exchange rates and stock indexes.

关键词： Financial time series forecasting Long short-term memory network adaboost algorithm Ensemble learning

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Short-term PV power prediction model based on weather feature clustering and adaboost-GA-BP 35

Short-term PV power prediction model based on weather featur...

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35th Chinese Control and Decision Conference (CCDC)

作者： Liu, Yujun He, Xinrui Duan, Shutong Wang, Bo Wang, Hongqing Beijing Forestry Univ Coll Sci Beijing Peoples R China Beijing Forestry Univ Sch Econ & Management Beijing Peoples R China China Elect Power Res Inst State Key Lab Operat & Control Renewable Energy & Beijing Peoples R China

ISBN: (纸本)9798350334722

Based on the weather feature clustering, an optimized algorithm called adaboost-GA-BN is presented for solving the problem that BE neural network is easy to fall into local extremum and over-fitting in PV power short-term prediction. Initially, the K-means algorithm is applied to cluster the historical data into sunny, cloudy and overcast&rainy day. It is then the GA that optimize initial weights, forming the GA-BP neural network. The idea of adaboost algorithm is applied to train GA-BP as different weak predictors so that the adaboost-GA-BP strong predictor is combined. Finally, this paper provides a detailed performances comparison of various models. The adaboost-GA-BN predicts a 4.4% decrease in MAE and a 4.1% decrease in RMSE for the sunny test set, a 9.0% decrease in MAE and a 13.5% decrease in RMSE for the cloudy test set, and a 2.7% decrease in MAE and a 9.1% decrease in RMSE for the rainy test set, outperforming the BP model, the GA-BP model, and the adaboost-BP models. After comparative analysis, the model has better prediction accuracy and generalization ability especially for predicting cloudy weather.

关键词： photopoltaic power short-term forecast back-propagation neural network genetic algorithm adaboost algorithm weather clustering

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Transfer adaboost SVM for Link Prediction in Newly Signed Social Networks using Explicit and PNR Features 19th

Transfer AdaBoost SVM for Link Prediction in Newly Signed So...

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19th International Conference on Knowledge Based and Intelligent Information and Engineering Systems (KES)

作者： Anh-Thu Nguyen-Thi Phuc Quang Nguyen Thanh Duc Ngo Tu-Anh Nguyen-Hoang Vietnam Natl Univ HCMC Univ Informat Technol Linh Trung Ward Ho Chi Minh City 700000 Vietnam

In signed social network, the user-generated content and interactions have overtaken the web. Questions of whom and what to trust has become increasingly important. We must have methods which predict the signs of links in the social network to solve this problem. We study signed social networks with positive links (friendship, fan, like, etc) and negative links (opposition, anti-fan, dislike, etc). Specifically, we focus how to effectively predict positive and negative links in newly signed social networks. With SVM model, the small amount of edge sign information in newly signed network is not adequate to train a good classifier. In this paper, we introduce an effective solution to this problem. We present a novel transfer learning framework is called Transfer adaboost with SVM (TAS) which extends boosting-based learning algorithms and incorporates properly designed RBFSVM (SVM with the RBF kernel) component classifiers. With our framework, we use explicit topological features and Positive Negative Ratio (PNR) features which are based on decision-making theory. Experimental results on three networks (Epinions, Slashdot and Wiki) demonstrate our method that can improve the prediction accuracy by 40% over baseline methods. Additionally, our method has faster performance time. (C) 2015 The Authors. Published by Elsevier B.V.

关键词： Link Prediction Signed Social Network adaboost algorithm

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Brain image segmentation based on improved BP-adaboost neural network

Brain image segmentation based on improved BP-Adaboost neura...

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International Forum on Medical Imaging in Asia

作者： Chao, Zhen Kim, Dohyeon Kim, Hee-joung Yonsei Univ Dept Radiat Convergence Engn Coll Hlth Sci Wonju South Korea Yonsei Univ Dept Radiol Sci Wonju South Korea Yonsei Univ Res Inst Hlth Sci Wonju South Korea

ISBN: (数字)9781510627765

ISBN: (纸本)9781510627765

The segmentation of medical image applying in medical anatomy plays an important role in various application. So, the study of medical image processing is very important and necessary. Due to the presence of noise and complexity of structure, the existing methods have various shortcomings and the performances are not ideal. In this study, we propose a new method which based on back propagation (BP) neural network and adaboost algorithm. The BP neural network we created is 1-7-1 structure. then we trained the system by Gravitational search algorithm (Here, we use the segmented images which were obtained by classic fuzzy c-means algorithm as the ideal output data). Based on this, we established and trained 10 groups of BPNN (We also call it as weak classifier) by applying 10 groups of different data. subsequently, we adopted the adaboost algorithm to obtain the weight of each BPNN. Finally, we made up a new BPA-daboost system for image segmentation. In this experiment, we used one group of datasets: Brain MRI. A comparison with the conventional segmentation method through subjective observation and objective evaluation indexes reveals that the proposed method achieved better results based on brain image segmentation.

关键词： Back propagation neural network adaboost algorithm Gravitational search algorithm

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Design and implementation of an adaboost-based Landlord bidding strategy 29

Design and implementation of an Adaboost-based Landlord bidd...

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第29届中国控制与决策会议

作者： Shuqin LI Meng DING Lukang PENG Shuai LIU Computer School BISTU

ISBN: (纸本)9781509046584

Landlords is a Chinese card game for all ages,each game requires at least three *** artificial intelligence service is growing rapidly to replace mankind to ensure that there are enough players online to play with people 24 hours a *** bidding is a vital part of this game,in this paper,we developed a bidding strategy by studying the real online gaming data from human's playing based on the adaboost *** choose 12 kinds of card combinations as weak classifiers,and train them with adaboost algorithm to get the final strong *** strong classifier can make an evaluation for a given hand,which can be used for making decisions whether bid or *** by different eigenvalue and card combinations data,we get an accuracy of 75%at most,basically reached the design requirements.

关键词： Landlords the bidding strategy card combination characteristics adaboost algorithm

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Study of Integrating adaboost and Weight Support Vector Regression Model

Study of Integrating AdaBoost and Weight Support Vector Regr...

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International Conference on Artificial Intelligence and Computational Intelligence

作者： Hu, Guo-Sheng Zhu, Feng-Feng Zhang, Ying-Chun Yu, Jin-Lian Shanghai Tech Inst Elect & Info Dept Comp Shanghai 201411 Peoples R China South China Univ Technol Dept Math Sci Guangzhou 510640 Peoples R China

ISBN: (纸本)9780769538167

The performance and regression precision of weak learners (accuracies should be greater than 0.5) for pattern recognition and forecasting can be upgraded using adaboost algorithm. Support vector machine (SVM) is a state-of-the-art learning machines and have been widely used in pattern recognition area since 90's of 20(th) contrary, however the performance of SVM is not stable and easily influenced due to the choice of parameters and kernel function. Hence in this paper, an integrated adaboost algorithm and weight support vector regression (WSVR) model are proposed. The proposed regression model has higher forecasting accuracy, and eliminates uncertain. The influence effects of training samples are not same, the later the training sample, more important to SVM classification ability. So we endow a different weight to each training sample. The proposed WSVR in this paper is an ideal learning machine for utilizing weighted samples, it is different from FSVM stemmed from Ref.[10]. The experimental results illustrate the proposed algorithm have better WME and WPE than single SVM algorithm and BPNN network.

关键词： adaboost algorithm weighted support vector regression electric power load forecasting error analysis

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Research of adaboost Face Detection and OpenCV Implementation 3

Research of AdaBoost Face Detection and OpenCV Implementatio...

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3rd International Conference on Advanced Engineering Materials and Architecture Science (ICAEMAS)

作者： Shi, Chunlei Yang, Dandan Jiang, Guangyuan Changchun Normal Univ Changchun Peoples R China

ISBN: (纸本)9783038352679

We introduce the principle of adaboost face detection algorithm based on Haar-like feature in detail, and use OpenCV face detection module to realize adaboost algorithm on CMU face library. Experimental results show that the algorithm receives good effects. Meanwhile we analyze the relationship between the number of simple classifier and face detection result.

关键词： face detection Haar-like feature Summed Area Table adaboost algorithm OpenCV

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Citrus Segmentation for Automatic Harvester Combined with adaboost Classifier and Leung-Malik Filter Bank

Citrus Segmentation for Automatic Harvester Combined with Ad...

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6th International-Federation-of-Automatic-Control (IFAC) Conference on Bio-Robotics (BIOROBOTICS)

作者： Lin, Guichao Zou, Xiangjun South China Agr Univ Guangzhou Guangdong Peoples R China

Image segmentation was the pre-processing step of fruit detection and positioning, and was challenging due to illumination change, occlusion, and cluttered background. To address segmentation problem, a novel segmentation method using adaboost classifier and texture-colour features was presented. The proposed method included two steps: (1) a fixed size sub-window was slid along every site in the image, and colour and texture features in the sub-window were extracted. Texture features were described by the Leung-Malik (LM) Filter Bank;(2) a strong classifier trained by adaboost algorithm was employed to assign every site a binary label. In order to train and evaluate the proposed method, a citrus dataset was provided which comprised 120 RGB images captured in natural illumination conditions. Twenty images in this dataset were used to train the adaboost classifier, and the remaining images were used as test set. Experiment on this test set showed that the proposed method achieved a precision of 0.867, and recall of 0.768, which confirms the validity of the proposed method. (C) 2018, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

关键词： adaboost algorithm Leung-Malik Filter Bank naive Bayes classifier image segmentation

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Efficient Improvement for adaboost based object detection

Efficient Improvement for Adaboost based object detection

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International Conference on Computational Intelligence and Natural Computing

作者： Luo Sheng Ye Xin-quan Wenzhou Univ Sch Mech & Elect Engn Wenzhou Zhejiang Prov Peoples R China

ISBN: (纸本)9780769536453

Object detection by the traditional adaboost algorithm is very time-consuming :mainly because the candidate feature number is large, and the feature number in the final strong detector is large. So this paper elevates 3 efficient optimization techniques and implements to reduce the training time. First we use some preprocessing technique to reduce the candidate features size to ten percent of the original, and then we use some implement skills to further reduce the training time. Besides these, we use double thresholds to describe each feature, which can improve the efficient of each feature, and reduce the required feature number for the final strong classifier. the experiment result show that the training of our system is hundred time faster than previous systems.

关键词： Object detection adaboost algorithm Optimization techniques Double thresholds

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