Thus, kernel-based algorithms can deal easily with interval data. The numerical test results with real and artificial datasets show that the proposed methods have given promising performance. We also use interactive graphical decision tree algorithms and visualization techniques to give an insight into ...
This is the first book that treats the fields ofsupervised, semi-supervised and unsupervised machine learningin a unifying way. In particular,it is the first presentation of the standard and improved graph based semisupervised (manifold) algorithms in a textbook. The book presents both the theory ...
So, in this paper, a framework of kernel adaptive filtering algorithms for indoor localization is constructed and the performance comparison between the proposed approach with both the conventional and the-state-of-the-art methods is carefully carried out. It is expected that the framework of ...
An Introduction to Kernel-Based Learning Algorithms.Provides an introduction to support vector machine (SVM), kernel Fisher discriminant (KFD) analysis and principal component analysis as examples for kernel-based learning methods. Basic concepts of learning theory; Nonlinear algorithms in kernel-feature ...
learning models and algorithms. This also makes it easier to achieve out-of-sample generalization. Moreover, we focus on learning metrics because this allows us to formulate the met- ric learning problembased on the kernel approach [Sch¨ olkopf ...
Kernel clusteringKernelization of the metricWidth hyper-parameterThe clustering performance of the conventional gaussian kernel based clustering algorithms are very dependent on the estimation of the width hyper-parameter of the gaussian kernel function. Usually this parameter is estimated once and for all...
...跟踪[132][133], 同时文献[134]提出基于核(Kernel-based)的 Mean Shift 跟踪算法。 ja.scribd.com|基于14个网页 2. 基于核函数的学习方法 又称有监督的学习方法,主要包括两大类:基于特征向量的学习方法(feature-based)和基于核函数的学习方法(kernel-based… ...
Many multi-instance learning algorithms have been intensively studied during this decade, such as the Diverse Density (DD) algorithm [6], multi-label multi-instance learning (MLMIL) [7], and neural network algorithm [8]. It is difficult to list all existing MIL algorithms. Here, we mainly ...
kernel methods and pattern analysis can be considered two of the most important topics in machine learning in the last few years. Their adaptability and modularity have given rise to a variety of kernels and algorithms in a number of topic areas. In particular, well-known algorithms have been ...
Also, two of the most widely used perfor- mance indexes have been modified using kernel distance function for the eval- uation of kernel based algorithms. Comparison between RFCM and proposed K-RFCM has been done on a wide variety of datasets to obtain favourable re- sults. From the ...