{"resolved":true,"match_type":"uuid","source_type":"original_book","book_id":"cd4f7678-cd9b-4971-bde0-e9ad46a6cc71","title":"人工智能与大数据技术导论","author":null,"edition":null,"publisher":null,"year":null,"isbn":"9787302517986","requested_by":"printed_page","book":{"source_type":"original_book","book_id":"cd4f7678-cd9b-4971-bde0-e9ad46a6cc71","id":"cd4f7678-cd9b-4971-bde0-e9ad46a6cc71","uuid":"cd4f7678-cd9b-4971-bde0-e9ad46a6cc71","title":"人工智能与大数据技术导论","normalized_title":"人工智能与大数据技术导论","author":null,"edition":null,"publisher":null,"year":null,"isbn":"9787302517986","isbns":["9787302517986"],"parsed":true,"ai_readable":true,"document_type":"book","page_count":490,"navigation_url":"https://workshop.closeclaw.top/ai/book/cd4f7678-cd9b-4971-bde0-e9ad46a6cc71","title_url":"https://workshop.closeclaw.top/ai/title/%E4%BA%BA%E5%B7%A5%E6%99%BA%E8%83%BD%E4%B8%8E%E5%A4%A7%E6%95%B0%E6%8D%AE%E6%8A%80%E6%9C%AF%E5%AF%BC%E8%AE%BA","fixed_page_url_template":"https://workshop.closeclaw.top/ai/book/cd4f7678-cd9b-4971-bde0-e9ad46a6cc71/page/{printed_page}","path_api_url_template":"https://workshop.closeclaw.top/api/ai/book/cd4f7678-cd9b-4971-bde0-e9ad46a6cc71/page/{printed_page}","read_url":"https://workshop.closeclaw.top/api/ai/read/cd4f7678-cd9b-4971-bde0-e9ad46a6cc71","page_url":"https://workshop.closeclaw.top/api/ai/books/cd4f7678-cd9b-4971-bde0-e9ad46a6cc71/page","search_url":"https://workshop.closeclaw.top/api/ai/books/cd4f7678-cd9b-4971-bde0-e9ad46a6cc71/search"},"page":{"printed_page":467,"pdf_page":483,"physical_index":483,"physical_index_base":0,"printed_page_status":"recognized","page_sha256":"eebbac4912e3279a2eefdfe098b9e80c52c4d6c6addd8a9a1b4550638b85705d"},"chapter":"第16章","content":"正则化率（regularization rate）\n\n一种标量值，以lambda表示，用于指定正则化函数的相对重要性。从下面简化的损失公式中可以看出正则化率的影响：\n\n$$\n\\mathrm { m i n i m i z e ( l o s s ~ f u n c t i o n + \\lambda \\rangle ( r e g u l a r i z a t i o n ~ f u n c t i o n ) } \\mathrm { ~ } )\n$$\n\n提高正则化率可以减少过拟合，但可能会使模型的准确率降低。\n\n表示法（representation）\n\n将数据映射到实用特征的过程。\n\nROC曲线(Receiver Operating Characteristic 曲线)\n\n不同分类阈值下的真正例率和假正例率构成的曲线。另可参阅曲线下面积。\n\n根目录（root directory）\n\n你指定的目录，用于托管多个模型的TensorFlow检查点和事件文件的子目录。\n\n均方根误差(Root Mean Squared Error，RMSE)\n\n均方误差的平方根。\n\nS\n\nSavedModel\n\n保存和恢复TensorFlow模型时建议使用的格式。SavedModel是一种独立于语言且可恢复的序列化格式，使较高级别的系统和工具可以创建、使用和转换TensorFlow模型。\n\nSaver\n\n一种TensorFlow对象，负责保存模型检查点。\n\n缩放(scaling）\n\n特征工程中的一种常用做法，是对某个特征的值区间进行调整，使之与数据集中其他特征的值区间一致。例如，假设你希望数据集中所有浮点特征的值都位于0到1的区间内，如果某个特征的值位于0到500的区间内，就可以通过将每个值除以500来缩放该特征。\n\n另可参阅标准化。\n\nscikit-learn\n\n一个热门的开源代码机器学习平台。可访问www.scikit-learn.org。\n\n半监督式学习(semi-supervised learning)\n\n训练模型时采用的数据中，某些训练样本有标签，而其他样本则没有标签。半监督式学习\n\n附录D 术语列表\n\n467","previous_page":466,"next_page":468}