1,723,354 research outputs found
Min jian yi ren biao yan
No full text86. XII, 坐船往宋村 -- 86. XII, 趙景琛演唱昆曲 -- 86. XII, 民間藝人表演.86. XII, Zuo chuan wang Song cun -- 86. XII, Zhao Jingchen yan chang Kun qu -- 86. XII, Min jian yi ren biao yan.1st subtitle supplied by cataloguer.Live recording.Electronic reproduction from Rulan Chao Pian V8 collection.Performers, unknown.Spoken and sung in Chinese
Mai tou ku gan (Bai yi ren)
No full text汪宗耀改作.Based on: Men in white / Sidney, Kingsley.On cover and Spine title: 埋頭苦幹(白衣人)本電子書乃根據《香港版權條例(第528章)》而複製, 並只可在大學圖書館系統內的獨立電子書系統上使用.Wang Zongyao gai zuo.Ben dian zi shu nai gen ju "Xianggang ban quan tiao li (Di 528 zhang)" er fu zhi, bing zhi ke zai da xue tu shu guan xi tong nei de du li dian zi shu xi tong shang shi yong.On cover and Spine title: Mai tou ku gan (Bai yi ren
The Default Risk of Firms Examined with Smooth Support Vector Machines
Get PDFIn the era of Basel II a powerful tool for bankruptcy prognosis is vital for banks. The tool must be precise but also easily adaptable to the bank's objections regarding the relation of false acceptances (Type I error) and false rejections (Type II error). We explore the suitabil- ity of Smooth Support Vector Machines (SSVM), and investigate how important factors such as selection of appropriate accounting ratios (predictors), length of training period and structure of the training sample in°uence the precision of prediction. Furthermore we show that oversampling can be employed to gear the tradeo® between error types. Finally, we illustrate graphically how di®erent variants of SSVM can be used jointly to support the decision task of loan o±cers.Insolvency Prognosis, SVMs, Statistical Learning Theory, Non-parametric Classification models, local time-homogeneity
Classification of multi-spectral image data considering non-Gaussian distribution and inter-pixel class dependency
No full textWood defect detection and classification using deep learning / Yap Yi Ren
Get PDFIn the timber and wood industry, natural defects on wood and timber are always one of the main issues. In many timber and wood industry, the quality assurance of the board is still controlled by a human. This is because the defects can vary in many ways likes amount, shape, area and colour. The quality checking process can be very tedious and worker may easily makes mistakes in judgement. To reduce the human mistakes, this study focuses on designing a wood defect detection and classification by using the artificial intelligence technique of Convolutional Neural Network (CNN) in MATLAB. Convolutional Neural Network (CNN) is one of the deep neural networks used in two-dimensional data. It mainly used to classify objects in images, cluster them by similarity and execute object recognition. This technology can identify faces, street sign, tumours, human, etc. The CNN model consists of input images, Convolution Layers, Activation Function (ReLU), Pooling, Fully Connected layers and Output layer. Three sets of input data such as Knots, Crack and Normal are prepared for training and testing the CNN model by using different parameters. The results of the different configurations are compared and analysed. The accuracy of overall classification is 97.2%
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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