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首页> 外文期刊>Neural Networks and Learning Systems, IEEE Transactions on >Feature Combiners With Gate-Generated Weights for Classification
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Feature Combiners With Gate-Generated Weights for Classification

机译：具有门生成权重的特征组合器进行分类

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摘要

Using functional weights in a conventional linear combination architecture is a way of obtaining expressive power and represents an alternative to classical trainable and implicit nonlinear transformations. In this brief, we explore this way of constructing binary classifiers, taking advantage of the possibility of generating functional weights by means of a gate with fixed radial basis functions. This particular form of the gate permits training the machine directly with maximal margin algorithms. We call the resulting scheme “feature combiners with gate generated weights for classification.” Experimental results show that these architectures outperform support vector machines (SVMs) and Real AdaBoost ensembles in most considered benchmark examples. An increase in the computational design effort due to cross-validation demands is the price to be paid to obtain this advantage. Nevertheless, the operational effort is usually lower than that needed by SVMs.
机译：在常规的线性组合体系结构中使用功能权重是获得表达能力的一种方式，它代表了经典的可训练和隐式非线性变换的替代方法。在本文中，我们探索了这种构造二进制分类器的方法，利用了通过具有固定径向基函数的门生成功能权重的可能性。门的这种特殊形式允许使用最大余量算法直接训练机器。我们称这种方案为“具有门产生权重以进行分类的特征组合器”。实验结果表明，在大多数被认为基准测试示例中，这些架构都优于支持向量机（SVM）和Real AdaBoost集成。由于交叉验证需求而导致的计算设计工作量的增加是要获得此优势的代价。尽管如此，操作工作量通常低于SVM所需的工作量。

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