{"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":173,"pdf_page":189,"physical_index":189,"physical_index_base":0,"printed_page_status":"recognized","page_sha256":"04bce721c6c56a1954e00f202e2694bbe986c6202aac03b1e84ec689dae45ef2"},"chapter":"第6章","content":"如图6-16所示的平面中的点显然无法用直线分开。即使引入松弛因子也不行，因为这本质上就不是线性可分的问题。但设想我们会使用某种“魔法”，能让这些点漂浮到空间中去，并且外围的点飘得更高，内圈的点飘得略低，就像图6-17一样。这样我们就可以用一张纸将这个漂浮的空间切断，实现两种点的分离。将这张纸还原到原平面中去是一条曲线。\n\n这个映射对应的规则是：\n\n在实际应用中，上述的空间变换是通过核变换实现的。核变换的表达形式与上面的公式不太相同。虽然SVM从本质上来说一种线性分类器，但Kernel SVM将核变换与线性分类器结合，使得SVM可以解决非线性问题。可以处理非线性边界问题是SVM最大的优势之一。\n\n6.3 逻辑回归算法\n\nLogisticRegression（逻辑回归）是一种常见的分类模型。我们不要被名字所惑，逻辑回归是一个分类模型，也就是说，要使用这个模型，标签数据必须是离散型变量。最简单的离散型变量是二元离散型变量（binaryvariable），即变量只取两个值：0或者1。逻辑回归之所以带有“回归”二字，是因为它以线性回归为基础演化而来，与线性回归的表达形式相似。\n\n让我们先回顾一下线性回归。线性回归用来刻画连续型变量y与若干变量 $^ { \\mathrm { x 1 , x 2 , } }$ ..之间的线性关系，其基本形式为：\n\n$$\n\\mathbf { y } = \\mathbf { a } 0 + \\mathbf { a } 1 \\mathbf { x } 1 + \\mathbf { a } 2 \\mathbf { x } 2 + \\ldots + \\mathbf { a } \\mathbf { m } \\mathbf { x } \\mathbf { m }\\tag{1}\n$$\n\n逻辑回归在上面公式的基础上对右侧施加一个叫作sigmoid的函数：\n\n$$\n\\mathbf { y } = \\mathbf { f } ( \\mathbf { a } 0 + \\mathbf { a } 1 \\mathbf { x } 1 + \\mathbf { a } 2 \\mathbf { x } 2 + \\ldots + \\mathbf { a } \\mathbf { m } \\mathbf { x } \\mathbf { m } )\\tag{2}\n$$\n\n$\\phi ( z ) = 1 / ( 1 + e ^ { - z } )$ 的图像如图6-18所示。\n\n第6章 机器学习算法\n\n173","previous_page":172,"next_page":174}