{"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":169,"pdf_page":185,"physical_index":185,"physical_index_base":0,"printed_page_status":"recognized","page_sha256":"9e6c51550b11fdc3570404a660cd63a4cb144e3b82940e1c805b855a496ef24d"},"chapter":"第6章","content":"LVQ可以表示为一组码本向量的集合。在开始的时候进行随机选择，通过多轮学习算法的迭代，最后得到与训练数据集最相配的结果。通过学习，码本向量可以像K最近邻算法那样进行预测。通过计算新数据样本与码本向量之间的距离找到最相似的邻居（最符合码本向量）。将最佳的分类值（或回归问题中的实数值）返回作为预测值。如果你将数据调整到相同的尺度，比如0和1，则可以得到最好的结果。如果你发现对于数据集，KNN有较好的效果，可以尝试一下LVQ来减少存储整个数据集对存储空间的依赖。\n\n6.1.8 支持向量机\n\n支持向量机（SVM）可能是最常用并且最常被谈到的机器学习算法。超平面是一条划分输入变量空间的线。在SVM中，选择一个超平面，它能最好地将输入变量空间划分为不同的类，要么是0，要么是1。在二维情况下，可以将它看作一根线，并假设所有输入点都被这根线完全分开。SVM通过学习算法找到最能完成类划分的超平面的一组参数。如图6-9所示为支持向量机的例子。\n\n第6章 机器学习算法\n\n169","previous_page":168,"next_page":170}