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SYSTEM AND METHOD FOR ONamp;minus;LINE TRAINING OF A SUPPORT VECTOR MACHINE

机译:支持向量机在线减法训练的系统和方法

摘要

A system and method for on-line training of a support vector machine (SVM). The SVM is trained with training sets from a stream of process data. The system detects availability of new training data, and constructs a training set from the corresponding input data. Over time, many training sets are presented to the SVM. When multiple presentations are needed to effectively train the SVM, a buffer of training sets is filled and updated as new training data becomes available. Once the buffer is full, a new training set bumps the oldest training set from the buffer. The training sets are presented one or more times each time a new training set is constructed. An historical database of time-stamped data may be used to construct training sets for the SVM. The SVM may be trained retrospectively by searching the historical database and constructing training sets based on the time-stamped data.
机译:一种用于支持向量机(SVM)的在线训练的系统和方法。使用来自过程数据流的训练集对SVM进行训练。系统检测到新训练数据的可用性,并根据相应的输入数据构建训练集。随着时间的流逝,许多训练集会呈现给SVM。当需要多次演示以有效地训练SVM时,将填充训练集的缓冲区并在新的训练数据可用时进行更新。一旦缓冲区已满,新的训练集就会从缓冲区中撞出最早的训练集。每次构建新的训练集时,都会对训练集进行一次或多次展示。时间戳数据的历史数据库可用于构造SVM的训练集。通过搜索历史数据库并基于时间戳数据构建训练集,可以对SVM进行追溯训练。

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