加以考量專家知識的單調性限制支援向量機在近年被提出，此種知識導向的技術已被證實能幫助支援向量機處理雜訊資料並得到更有用的結果。然而加入單調性限制後的支援向量機透過使用二次規劃的方法計算矩陣，在訓練大資料集時將產生相當大的運算成本。
因此本研究利用遞增策略針對大資料集進行求解，將資料集視為串流資料，進而將資料點逐筆訓練。提出遞增式單調性限制支援向量機，針對大型及高複雜資料以遞增策略求解，增加支援向量機處理大量資料的可行性。
本研究實驗結果發現，以遞增策略進行求解的單調性限制支援向量機擁有更快速的訓練時間，同時兼顧原本模型應有的正確率。在串流資料的實驗發現，因為所需較少訓練資料且不須重新訓練，遞增式單調性限制支援向量機在資料集逐漸增加時，擁有更佳訓練時間效益。

RMC-SVM that incorporates experts' knowledge into SVMs based on the monotonic property of real-world problems has been proposed recently. This knowledge-oriented technique help SVMs to deal with noisy data and gain more useful results. However, solving SVMs with monotonicity constraints will require even more computational time than SVMs especially on large-scale datasets.
Therefore, in this research, an incremental strategy is formulated to handle large datasets. We can treat large-scale dataset as streaming data, constructing the solution continuously, one point at one time. This study proposes incremental RMC-SVM to increase the feasibility of using RMC-SVMs in real world applications.
Our experiments show the training time of the incremental RMC-SVM is less than the training time of RMC-SVM when both have the similarly classified results. In streaming case, OI-MCSVM has better performance when datasets growing up, since there is much less training set to be trained and no need to retrain the previous data points.

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