论文标题
机器学习的傅立叶变换方法III：傅立叶分类
Fourier Transform Approach to Machine Learning III: Fourier Classification
论文作者
论文摘要
我们为高度非线性多类分类提出了一种基于傅立叶的学习算法。该算法基于一种平滑技术来计算所有类别的概率分布。为了获得概率分布，每个类的密度分布分别通过低通滤波器平滑。傅立叶表示的优点是捕获数据分布的非线性，而无需定义任何内核函数。此外，与支持向量机相反，它为分类提供了一个概率的解释。此外，它也可以治疗重叠的课程。与逻辑回归相比，它不需要功能工程。通常，它的计算性能对于大型数据集也很好，与其他算法相反，典型的过度拟合问题根本不会发生。该算法的能力用于多类分类，具有重叠的类和类别分布的非常高的非线性。
We propose a Fourier-based learning algorithm for highly nonlinear multiclass classification. The algorithm is based on a smoothing technique to calculate the probability distribution of all classes. To obtain the probability distribution, the density distribution of each class is smoothed by a low-pass filter separately. The advantage of the Fourier representation is capturing the nonlinearities of the data distribution without defining any kernel function. Furthermore, contrary to the support vector machines, it makes a probabilistic explanation for the classification possible. Moreover, it can treat overlapped classes as well. Comparing to the logistic regression, it does not require feature engineering. In general, its computational performance is also very well for large data sets and in contrast to other algorithms, the typical overfitting problem does not happen at all. The capability of the algorithm is demonstrated for multiclass classification with overlapped classes and very high nonlinearity of the class distributions.
