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Titlebook: Lesbians in Television and Text after the Millennium; Rebecca Beirne Book 2008 Palgrave Macmillan, a division of Nature America Inc. 2008

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發(fā)表于 2025-3-21 16:08:00 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱Lesbians in Television and Text after the Millennium
編輯Rebecca Beirne
視頻videohttp://file.papertrans.cn/586/585186/585186.mp4
圖書封面Titlebook: Lesbians in Television and Text after the Millennium;  Rebecca Beirne Book 2008 Palgrave Macmillan, a division of Nature America Inc. 2008
描述Taking up such issues as mainstreaming, the male gaze, and female masculinity, this book puts forward provocative readings of little explored texts and offers new insights into the contemporary representation of lesbians.
出版日期Book 2008
關鍵詞politics; pornography; production; television
版次1
doihttps://doi.org/10.1057/9780230615014
isbn_softcover978-1-349-37411-3
isbn_ebook978-0-230-61501-4
copyrightPalgrave Macmillan, a division of Nature America Inc. 2008
The information of publication is updating

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Rebecca Beirner to perfectly lead and manage multicultural teams. The study is exploratory in nature. The study targets the people belonging to different cultural and working as a team in different organizations. The data is collected through questionnaire from 70 respondents belonging to 10 diverse multicultural
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Rebecca Beirnetize the null values, eliminating the duplicate and unwanted columns. The cleaned dataset is then assessed to construct the novel custom features and basic features for the attack detection, which represent the feature vector. Novel features are constructed to reduce the learning confusion of machin
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Rebecca Beirnepre-processing technique to get a minimum number of attributes rather than using all attributes in the dataset which has impact on the accuracy of classifiers. After that pre-processed data is trained by using various classifiers like linear regression, SVM, na?ve Bayes, and decision tree, and then
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recognition prototype is defined very well. This prototype consists of many vital steps built using most advanced methods: CNN cascade for detection of face and HOG for generating face embeddings. The primary aim of this analysis was the sensible use of those developing deep learning techniques for
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