Supervised learning of heuristic function for refutation 反演啟發(fā)函數(shù)的監(jiān)督學習算法
The former belongs to supervised learning and the latter belongs to unsupervised learning 它們分屬于有監(jiān)督學習與無監(jiān)督學習。
A semi - supervised learning system was proposed based on art ( adaptive resonance theory ) 摘要根據(jù)自適應諧振理論提出了半監(jiān)督學習自適應諧振理論系統(tǒng)。
Supervised learning with the use of regression and classification networks with sparse data sets will be explored 也將在課程中以帶有稀疏值理論的分類神經(jīng)網(wǎng)絡與回歸的使用來探討監(jiān)督式學習。
The distinct difference between supervised learning and unsupervised learning lies in whether the example consists of the pre - processed output value 這兩種方法最大的區(qū)別就在于學習樣本是否包含有預先規(guī)定好的輸出值。
Supervised learning is the machine learning task of inferring a function from labeled training data. The training data consist of a set of training examples.