Seventh week of machine learning on Coursera
如果訓練集樣本線性可分，那麼在樣本空間肯定能找到一個劃分超平面將正負樣本分隔開。 在之前學習的邏輯斯蒂迴歸(感知機)基於梯度下降的方法來求得這個超平面。但是這種方法求得的超平面是無窮個的（指的是感知機使用梯度下降迭代更新 ω和b 的過程中，會產生很多超平面方程，具體可參考《統計學習方法》例2.1），那麼如何才能找到最優的超平面呢？ 由此就引出了我們這節的主題—Support Vector Mach
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相關文章
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