{"resolved":true,"match_type":"uuid","source_type":"original_book","book_id":"cf76bc49-7e9b-4f42-bee8-3050a9fb9feb","title":"智慧的疆界 从图灵机到人工智能","author":null,"edition":null,"publisher":null,"year":null,"isbn":"9787111778004","requested_by":"printed_page","book":{"source_type":"original_book","book_id":"cf76bc49-7e9b-4f42-bee8-3050a9fb9feb","id":"cf76bc49-7e9b-4f42-bee8-3050a9fb9feb","uuid":"cf76bc49-7e9b-4f42-bee8-3050a9fb9feb","title":"智慧的疆界 从图灵机到人工智能","normalized_title":"智慧的疆界从图灵机到人工智能","author":null,"edition":null,"publisher":null,"year":null,"isbn":"9787111778004","isbns":["9787111778004","9787111695714"],"parsed":true,"ai_readable":true,"document_type":"book","page_count":456,"navigation_url":"https://workshop.closeclaw.top/ai/book/cf76bc49-7e9b-4f42-bee8-3050a9fb9feb","title_url":"https://workshop.closeclaw.top/ai/title/%E6%99%BA%E6%85%A7%E7%9A%84%E7%96%86%E7%95%8C%20%E4%BB%8E%E5%9B%BE%E7%81%B5%E6%9C%BA%E5%88%B0%E4%BA%BA%E5%B7%A5%E6%99%BA%E8%83%BD","fixed_page_url_template":"https://workshop.closeclaw.top/ai/book/cf76bc49-7e9b-4f42-bee8-3050a9fb9feb/page/{printed_page}","path_api_url_template":"https://workshop.closeclaw.top/api/ai/book/cf76bc49-7e9b-4f42-bee8-3050a9fb9feb/page/{printed_page}","read_url":"https://workshop.closeclaw.top/api/ai/read/cf76bc49-7e9b-4f42-bee8-3050a9fb9feb","page_url":"https://workshop.closeclaw.top/api/ai/books/cf76bc49-7e9b-4f42-bee8-3050a9fb9feb/page","search_url":"https://workshop.closeclaw.top/api/ai/books/cf76bc49-7e9b-4f42-bee8-3050a9fb9feb/search"},"page":{"printed_page":211,"pdf_page":226,"physical_index":226,"physical_index_base":0,"printed_page_status":"recognized","page_sha256":"1fc5202ff2ef79c9b6b0d83ca77f324f84e07a8816cda7b6532f9827088520a8"},"chapter":"内容简介","content":"非垃圾邮件垃圾邮件\n\n图 6-22 中列举了线性、规则圆形、不规则曲线三种模型，为了方便定量地讨论这个问题，笔者把上面三个模型对应的函数表达式也列出来，如下所示：\n\n$$\n{ \\begin{array} { r l } & { 1 ) ~ f ( x ) = w _ { 0 } + w _ { 1 } x _ { 1 } + w _ { 2 } x _ { 2 } } \\\\ & { 2 ~ ) ~ f ( x ) = w _ { 0 } + w _ { 1 } x _ { 1 } + w _ { 2 } x _ { 2 } + w _ { 3 } x _ { 1 } ^ { 2 } + w _ { 4 } x _ { 2 } ^ { 2 } } \\\\ & { 3 ~ ) ~ f ( x ) = w _ { 0 } + w _ { 1 } x _ { 1 } + w _ { 2 } x _ { 2 } + w _ { 3 } x _ { 1 } ^ { 2 } + w _ { 4 } x _ { 2 } ^ { 2 } + w _ { 5 } x _ { 1 } x _ { 2 } + w _ { 6 } x _ { 1 } ^ { 2 } x _ { 2 } + w _ { 7 } x _ { 1 } x _ { 2 } ^ { 2 } + w _ { 8 } x _ { 1 } x _ { 2 } } \\\\ & { w _ { 8 } x _ { 1 } ^ { 3 } + w _ { 9 } x _ { 2 } ^ { 3 } + \\cdots } \\end{array} }\n$$\n\n部分读者可能又会有疑问：第 1、2 个表达式尚可以说是中学解析几何中直线和圆的标准方程形式，但对于第 3 个表达式，函数可能用到的数学工具很多，诸如 $\\boldsymbol { \\mathrm { e } } ^ { x }$ 、log x、ln x、1/x 这些都是很常见的，为什么这样一个复杂的曲线模型的函数必须是简单多项式的形式呢？这个问题的答案既复杂又简单，如果读者已经不记得高数课本中讲过的泰勒多项式展开的话，那就直接记住这个结论好了：所有光滑的函数图像都可以使用泰勒公式以任意精度去逼近、模拟，展开成泰勒多项式的形式。所以，不论现实中决策函数是由 $\\boldsymbol { \\mathrm { e } } ^ { x }$ 、log x、ln x、1/x、sin x 还是由其他任何形式的可导复杂项构成的，都可以使用简单多项式形式，以任意要求的精度模拟出来。\n\n第 6 章 机器学习概览\n\n211","previous_page":210,"next_page":212}