{"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":178,"pdf_page":193,"physical_index":193,"physical_index_base":0,"printed_page_status":"recognized","page_sha256":"ebda71da30df34a8d20485a005e082809d9e74f0349bc6cf5d2646636da6e261"},"chapter":"内容简介","content":"还有一种分类方式是按照机器学习输出模型的可解释性进行划分。可解释性决定了学习的最终目标是透过现象找到背后的运行规律，还是只要找出特征与现象间的隐含关系就行了。机器学习在这方面的发展历程完全贴合了人工智能研究从最开始探究规则到后来主要依靠仿生模拟的发展过程，大体上，研究的目标最初是白盒可解释的学习，主要是基于规则、树结构或线性回归的机器学习，随着符号主义的衰落，研究的主要目标正在朝着黑盒不可解释的学习方向持续倾斜，主要是基于神经网络、概率统计的机器学习。笔者对上述提到的三种分类方式中出现的机器学习方法进行了总结，如图 6-1 所示。\n\n图 6-1 中白盒可解释的学习在本书第 3 章讲解符号主义的“学习期”时已经初步涉及一些，目前它仍然只能处理比较浅层的、直接藏于数据背后的简单规律，很难处理需要经过思考和复杂推理才能得出的深层的、本质的规律。这条分支现已不算是机器学习的主流，有些学者甚至提倡摒弃这部分内容，这当然是有失偏颇的，但由于篇幅所限，笔者并没有把这部分内容拆分成单独的章节来讲解<sup>○一</sup>。除此之外，图中的其余内容均是第三部分（第 6 章至第 8 章）的重点，笔者会在后续章节一一展开介绍。\n\n178\n\n第三部分 第三波高潮","previous_page":177,"next_page":179}