{"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":166,"pdf_page":182,"physical_index":182,"physical_index_base":0,"printed_page_status":"recognized","page_sha256":"7455499703876aba4a33ad9de3afbbf643249bf9a007d87f64b34cc1749cc28a"},"chapter":"第6章","content":"6.1.3线性判别分析\n\n逻辑回归是一个处理二分类问题的传统分类算法。如果需要进行更多的分类，线性判别分析算法（LinearDiscriminant Analysis，LDA）是一个更好的线性分类方法。线性判别分析的例子如图6-4所示。对LDA的解释非常直接，它包括针对每一个类的输入数据的统计特性。对于单一输入变量来说，包括：\n\n类内样本均值。\n\n●总体样本变量。\n\n通过计算每个类的判别值，并根据最大值来进行预测。这种方法假设数据服从高斯分布（钟形曲线），所以可以较好地提前去除离群值。这是针对分类模型预测问题的一种简单有效的方法。\n\n人工智能与大数据技术导论\n\n166","previous_page":165,"next_page":167}