检索结果-南通市图书馆

Boosted Stacking Ensemble Machine Learning Method for Wafer Map pattern classification

在线全文

学校读者我要写书评

暂无评论

Computers, Materials & Continua 2023年第2期74卷 2945-2966页

作者： Jeonghoon Choi Dongjun Suh Marc-Oliver Otto Department of Convergence and Fusion System Engineering Kyungpook National UniversitySangju37224Korea Department of Mathematics Natural and Economic SciencesUlm University of Applied SciencesUlm89075Germany

Recently,machine learning-based technologies have been developed to automate the classification of wafer map defect patterns during *** existing approaches used in the wafer map pattern classification include directly learning the image through a convolution neural network and applying the ensemble method after extracting image *** study aims to classify wafer map defects more effectively and derive robust algorithms even for datasets with insufficient defect ***,the number of defects during the actual process may be ***,insufficient data are generated using convolutional auto-encoder(CAE),and the expanded data are verified using the evaluation technique of structural similarity index measure(SSIM).After extracting handcrafted features,a boosted stacking ensemble model that integrates the four base-level classifiers with the extreme gradient boosting classifier as a meta-level classifier is designed and built for training the model based on the expanded data for final *** the proposed algorithm shows better performance than those of existing ensemble classifiers even for insufficient defect patterns,the results of this study will contribute to improving the product quality and yield of the actual semiconductor manufacturing process.

关键词： Wafer map pattern classification machine learning boosted stacking ensemble semiconductor manufacturing processing

SVD-LSSVM and its application in chemical pattern classification

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Journal of Zhejiang University-Science A(Applied Physics & Engineering) 2006年第11期7卷 1942-1947页

作者： TAO Shao-hui CHEN De-zhao HU Wang-ming Department of Chemical Engineering Zhejiang University Hangzhou 310027 China

pattern classification is an important field in machine learning; least squares support vector machine (LSSVM) is a powerful tool for pattern classification. A new version of LSSVM, SVD-LSSVM, to save time of selecting hyper parameters for LSSVM is proposed. SVD-LSSVM is trained through singular value decomposition (SVD) of kernel matrix. Cross validation time of selecting hyper parameters can be saved because a new hyper parameter, singular value contribution rate (SVCR), replaces the penalty factor of LSSVM. Several UCI benchmarking data and the Olive classification problem were used to test SVD-LSSVM. The result showed that SVD-LSSVM has good performance in classification and saves time for cross validation.

关键词： pattern classification Structural risk minimization Least squares support vector machine (LSSVM) Hyper pa-rameter selection Cross validation Singular value decomposition (SVD)

Weighted Linear Loss Large Margin Distribution Machine for pattern classification

在线全文

学校读者我要写书评

暂无评论

Chinese Journal of Electronics 2024年第3期33卷 753-765页

作者： Ling LIU Maoxiang CHU Rongfen GONG Liming LIU Yonghui YANG School of Electronic and Information Engineering University of Science and Technology Liaoning

Compared with support vector machine,large margin distribution machine(LDM) has better generalization *** central idea of LDM is to maximize the margin mean and minimize the margin variance *** the computational complexity of LDM is *** order to reduce the computational complexity of LDM,a weighted linear loss LDM(WLLDM) is *** framework of WLLDM is built based on LDM and the weighted linear *** weighted linear loss is adopted instead of the hinge loss in *** modification can transform the quadratic programming problem into a simple linear equation,resulting in lower computational ***,WLLDM has the potential to deal with large-scale *** WLLDM is similar in principle to the LDM algorithm,which can optimize the margin distribution and achieve better generalization *** WLLDM algorithm is compared with other models by conducting experiments on different *** results show that the proposed WLLDM has better generalization performance and faster training speed.

关键词： Training Support vector machines pattern classification Transforms Fasteners Mathematical models Vectors

pattern classification in Chinese Medicine

在线全文

同方期刊数据库

学校读者我要写书评

暂无评论

Pattern Classification in Chinese Medicine

Bridging the Traditional Chinese Medicine pattern classification and Biomedical Disease Diagnosis with Systems Biology

第十八届中国科协年会——分16 针灸大科学研究学术高峰论坛

作者： Aiping LU School of Chinese Medicine Hong Kong Baptist University

pattern classification, the core theory in Chinese medicine clinical practice, will be a complementary method to classify individual?s status and contribute to personalized medicine. The author provided a logical argument focuses on the innovation that may result from integrated pattern classification and biomedical diagnosis, and introduced some novel research design and methods of pattern classification researches using systemic biological approaches and evidence-based medicine design, as well as developed novel approaches on the methodology of clinical effectiveness evaluation on Chinese medicine and new drug research and development.

关键词： pattern classification Chinese medicine system biology evidence-based medicine personalized medicine

来源： cnki会议评论

在线全文

cnki会议

学校读者我要写书评

暂无评论

Chinese Journal of Integrative Medicine 2012年第12期18卷 883-890页

作者：吕爱平卞兆祥陈可冀 School of Chinese Medicine Hong Kong Baptist University China Academy of Chinese Medical Sciences

Being the unique core of traditional Chinese medicine （TCM）, pattern classification exerts a direct effect on the efficacy and safety of herbal interventions. In this article, the authors integrated the pattern classification and disease diagnosis with many approaches from systems biology, integration of pattern classification with biomedical diagnosis by systems biology is not only a new direction of personalized medicine development, but also provides a new drug development model. In the further study, the pattern classifications of major diseases will be the focus of research.

关键词： traditional Chinese medicine pattern classification systems biology biomedical diagnosis

Intracranial Epileptic Seizures Detection Based on an Optimized Neural Network Classifier

在线全文

学校读者我要写书评

暂无评论

Chinese Journal of Electronics 2021年第3期30卷 419-425页

作者： GONG Chen LIU Jiahui NIU Yunyun School of Information Engineering China University of Geosciences in Beijing Institute of Automation Chinese Academy of Sciences School of Artificial Intelligence University of Chinese Academy of Sciences

Automatic identification of intracranial electroencephalogram(i EEG) signals has become more and more important in the field of medical *** this paper, an optimized neural network classifier is proposed based on an improved feature extraction method for the identification of i EEG epileptic *** kinds of entropy, Sample entropy, Approximate entropy, Shannon entropy, Log energy entropy are extracted from the database as the feature vectors of Neural network(NN) during the identification *** kinds of classification tasks, namely Pre-ictal v Post-ictal(CD), Pre-ictal v Epileptic(CE), Post-ictal v Epileptic(DE), Pre-ictal v Post-ictal v Epileptic(CDE),are used to test the effect of our classification *** experimental results show that our algorithm achieves higher performance in all tasks than previous *** effect of hidden layer nodes number is investigated by a constructive approach named growth method. We obtain the optimized number ranges of hidden layer nodes for the binary classification problems CD, CE, DE, and the multitask classification problem CDE, respectively.

关键词： improved feature extraction method medical signal processing optimized neural network classifier classification method classification tasks identification process intracranial electroencephalogram signals feature extraction medical diagnostics entropy feature vectors intracranial Epileptic seizures detection optimized number ranges Approximate entropy Log energy entropy neural nets Pre-ictal v Post-ictal medical signal detection constructive approach named growth method automatic identification pattern classification Shannonentropy electroencephalography Sample entropy

Malware Detection Algorithm Based on the Attention Mechanism and ResNet

在线全文

同方期刊数据库

学校读者我要写书评

暂无评论

Chinese Journal of Electronics 2020年第6期29卷 1054-1060页

作者： WANG Lele WANG Binqiang ZHAO Peipei LIU Ruyi LIU Jiangang MIAO Qiguang Institute of Information Technology Information Engineering University School of Computer Science and Technology Xidian University Nanjing Institute of Information Technology

The cost of misclassifying a malware program as normal is often higher than that of misclassifying a normal program as ***,how to improve the detection accuracy of malware programs is a very important *** paper proposes a deep learning malware program detection algorithm based on attention *** 2 Vec model is used to map the Application programming interface(API) into word vectors,and all word vectors of each sample are arranged into a matrix with the same *** this basis,residual network is used to extract features of *** features are input into the attention mechanism to learn the similarity between ***,the features are weighted with the similarity to obtain the new features with better *** new features and the original features are added element by element to obtain the sample features more suitable for ***,samples are classified by *** show that the classification effect of the proposed method is better than that of the traditional machine learning method.

关键词： application program interfaces feature extraction invasive software learning (artificial intelligence) pattern classification text analysis machine learning method API deep learning malware program detection accuracy normal program malware detection algorithm sample features word vectors application programming interface word2Vec model attention mechanism Malware program Word2Vec Residual network structure Self-attention mechanism

An approach for automatic sleep stage scoring and apnea-hypopnea detection

在线全文

同方期刊数据库

学校读者我要写书评

暂无评论

Frontiers of Computer Science 2012年第2期6卷 230-241页

作者： TimSCHLǔTER StefanCONRAD InstituteofComputerScience HeinrichHeineUniversity40225DtisseldorfGermany

In this article we present an application of data mining to the medical domain sleep research, an approach for automatic sleep stage scoring and apnea-hypopnea detec- tion. By several combined techniques （Fourier and wavelet transform, derivative dynamic time warping, and waveform recognition）, our approach extracts meaningful features （fre- quencies and special patterns like k-complexes and sleep spindles） from physiological recordings containing EEG, ECG, EOG and EMG data. Based on these pieces of in- formation, an ensemble of decision trees is constructed us- ing the principle of bagging, which classifies sleep epochs in their sleep stages according to the rules by Rechtschaf- fen and Kales and annotates occurrences of apnea-hypopnea （total or partial cessation of respiration）. After that, case- based reasoning is applied in order to improve quality. We tested and evaluated our approach on several large public databases from PhysioBank, which showed an overall accu- racy of 95.2% for sleep stage scoring and 94.5% for classify- ing minutes as apneic or non-apneic.

关键词： time series data processing signal processing,feature extraction pattern classification biomedical signalprocessing sleep

Adaptive segmentation based on multi-classification model for dermoscopy images

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Frontiers of Computer Science 2015年第5期9卷 720-728页

作者： Fengying XIE Yefen WU Yang LI Zhiguo JIANG Rusong MENG Image Processing Center School of Astronautics Beihang University Beijing 100191 China General Hospital of the Air Force People's Liberation Army Beijing 100036 China

Segmentation accuracy of dermoscopy images is important in the computer-aided diagnosis of skin cancer and a wide variety of segmentation methods for dermoscopy images have been developed. Considering that each method has its strengths and weaknesses, a novel adaptive segmentation framework based on multi-classification model is proposed for dermoscopy images. Firstly, five patterns of images are summarized according to the factors influencing segmentation. Then the matching relation is established between each image pattern and its optimal segmentationmethod. Next, the given image is classified into one of the five patterns by the multi-classification model based on BP neural network. Finaily, the optimal segmentation method for this image is selected according to the matching relation, and then the image is effectively segmented. Experiments show that the proposed method delivers better accuracy and more robust segmentation results compared with the other seven state-of-the-art methods.

关键词： adaptive segmentation feature extraction pattern classification dermoscopy image