Probabilistic subspace mixture models, as proposed over the last few years, are interesting methods for learning image manifolds, i.e. nonlinear subspaces of spaces in which images are represented as vectors by their grey-values. Their lack of a glo
This book is a graduate-level textbook on data structures. A data structure is a method1 to realize a set of operations on some data. The classical example is to keep track of a set of items, the items identified by key values, so that we can insert
Abstract Support vector machine (SVM) is a popular technique for classi cation. However, beginners who are not familiar with SVM often get unsatisfactory results since they miss some easy but signi cant steps. In this guide, we propose a simple proc
演化硬件在声纳识别中的应用 Abstract: An evolvable hardware (EHW) system for high-speed sona return classification has been proposed. The system demonstrates an average accuracy of 91.4% on a sonar spectrum data set. This is bette than a feed-forward neural netwo
Abstract—This paper introduces a multilinear principal com- ponent analysis (MPCA) framework for tensor object feature extraction. Objects of interest in many computer vision and pattern recognition applications, such as 2-D/3-D images and video seq
This book presents the basic tools of modern analysis within the context of what might be called the fundamental problem of operator theory: to calculate spectra of specific operators on infinite-dimensional spaces, especially operators on Hilbert spa
Various black-box methods for the generation of test cases have been proposed in the literature. Many of these methods, including the category-partition method and the classi cation-tree method, follow the approach of partition testing, in which the
The problem of resolving the identity of a person can be categorized into two fundamentally dis- tinct types of problemswith different inherent complexities [1]: (i) verification and (ii) recognition. Verification (authentication) refers to the proble
SVMs (Support Vector Machines) are a useful technique for data classi cation. Al- though SVM is considered easier to use than Neural Networks, users not familiar with it often get unsatisfactory results at rst. Here we outline a \cookbook" approach
