{"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":298,"pdf_page":314,"physical_index":314,"physical_index_base":0,"printed_page_status":"recognized","page_sha256":"b5cdb659ba358fbe463379ddba043a637eeee5969ec5f29cc2c52d0948253051"},"chapter":"第9章","content":"结果如图9-35、图9-36所示。\n\n9.5 L1正则化\n\n我们再来研究一下特征交叉组合（FeatureCross）。特征交叉组合很有用，但也可能会带来一些问题，尤其是将稀疏特征交叉组合起来的时候。稀疏矢量通常包含许多维度，创建特征交叉组合会导致包含更多维度。由于使用此类高维度特征矢量，因此模型可能会非常庞大，并且需要大量的RAM。在高维度稀疏矢量中，最好尽可能使权重正好降至0。正好为0的权重基本上会使相应特征从模型中移除。将特征权重设为0可节省RAM空间，且可以减少模型中的噪点。例如，以一个涵盖全世界（不只是涵盖加州）的住房数据集为例。如果按分（每度为60分）对全球纬度进行分桶，则在一次稀疏编码过程中会产生大约1万个维度；如果按分对全球经度进行分桶，则在一次稀疏编码过程中会产生大约2万个维度。这两种特征的特征交叉组合会产生大约2亿个维度。这2亿个维度中，很多维度代表非常有限的居住区域（例如海洋里），很难使用这些数据进行有效泛化。如果为这些不需要的维度浪费RAM，就太不值得了。因此，最好使无意义维度的权重正好降至0。\n\n人工智能与大数据技术导论\n\n298","previous_page":297,"next_page":299}