A method for classifier generation includes a step of obtaining data for classification of a multitude of samples, the data for each of the samples consisting of a multitude of physical measurement feature values and a class label. Individual mini-classifiers are generated using sets of features from the samples. The performance of the mini-classifiers is tested, and those that meet a performance threshold are retained. A master classifier is generated by conducting a regularized ensemble training of the retained/filtered set of mini-classifiers to the classification labels for the samples, e.g., by randomly selecting a small fraction of the filtered mini-classifiers (drop out regularization) and conducting logistical training on such selected mini-classifiers. The set of samples are randomly separated into a test set and a training set. The steps of generating the mini-classifiers, filtering and generating a master classifier are repeated for different realizations of the separation of the set of samples into test and training sets, thereby generating a plurality of master classifiers. A final classifier is defined from one or a combination of more than one of the master classifiers.一種用於分類器產生之方法包括獲得用於分類眾多樣本之資料之步驟,用於該等樣本中的每一者之該資料由眾多物理量測特徵值及類別標籤組成。使用來自該等樣本之特徵集產生個別微分類器。測試該等微分類器之效能,且保留滿足效能臨限值之彼等微分類器。藉由進行微分類器之經保留/經篩選集合關於該等樣本之該等分類標籤的規則化集體訓練而產生主分類器,例如藉由隨機選擇經篩選微分類器之一小部分(丟棄規則化)及對此等選定微分類器進行邏輯訓練。將該樣本集隨機分離成測試集及訓練集。針對分離該樣本集至測試集及訓練集之不同實現而重複產生該等微分類器、篩選及產生主分類器之步驟,藉此產生複數個主分類器。自該等主分類器中的一者或一者以上之組合定義最終分類器。1100‧‧‧資料之分類器開發集1102‧‧‧步驟1104‧‧‧「早期」群組1106‧‧‧「晚期」群組1108‧‧‧步驟1110‧‧‧測試集1112‧‧‧訓練集1120‧‧‧分類器開發步驟1122‧‧‧縮減之特徵空間1124‧‧‧方框1126‧‧‧分類器開發步驟1128‧‧‧步驟1130‧‧‧分類器開發步驟1132‧‧‧步驟1134‧‧‧步驟1136‧‧‧迴圈/步驟1138‧‧‧步驟1140‧‧‧步驟1142‧‧‧步驟1144‧‧‧步驟
