[PDF] Top 20 Support Vector Machines for Design Space Exploration

... Another important issue is how the new point is ap- proached. To avoid engine damage the engine’s actuating variables are changed slowly and usually in small discrete steps. After each step the procedure is halted for a ... See full document

... Although the internal approach to SVM (see svm-internal) clustering is only weakly biased towards the shape of the clusters in the input space (the bias is for spherical clusters in the feature space), it ... See full document

... The most interesting feature of BN [40, 43], compared to decision trees is the pos- sibility of taking into account prior information about a given problem and the simple structure lends itself to comprehensible ... See full document

... Recently, SVMs have been a capable instrument for area categorization. Its fundamental idea is to represent data into a high dimensional space and find a splitting hyper plane with the greatest boundary. SVMs have ... See full document

... Each stack filter based on a positive Boolean function can be characterized by two properties—threshold decomposi- tion and stacking property [11, 22]. The Boolean function on which each WOS filter is based is a ... See full document

... the design of fault- tolerant embedded systems and is supported by an infrastructure that permits to easily combine hardware/software soft errors mitigation ...the design space exploration, ... See full document

... The remaining sections are organized as follows: A review of coarse-to-fine object detection, including related work on cascades, is presented in Section 2. In Section 3, we discuss hierarchical representation and search ... See full document

... the space. For example, this intuition underlies the Transductive Support Vector Machines (Vapnik, 2000) and its different implementations, such as TSVM (Joachims, 1999) or S 3 VM (Demiriz and ... See full document

... for support vector machines in a hybrid Data Mining and Case-Based Reasoning system which incorporates a vector model to help transfer textual information to numerical vector in order ... See full document

... The results show that SVMs are capable of classifying unit quaternions representing the orientations of a virtual anatomical limb as either valid or invalid. Hence they implicitly model the boundary between valid and ... See full document

... The pattern search algorithm is embedded in Algorithm 1 as a sub-routine. Each iteration of a pattern search algorithm starts with a center (initially randomly chosen), samples other points around the center in the ... See full document

... Abstract— Speaker independent speech recognition system has been a challenging field of research since speech is the most basic and natural means of communication. In this work, a speech recognition system is developed ... See full document

... decision space such that the margin of separation between positive and negative samples is ...the support vector machines are an approximate implementation of the method of structural risk ... See full document

... We need to point out that there are several different definitions of robustness in the literature. In this paper, as well as the aforementioned robust classification papers, robustness is mainly under- stood from a ... See full document

... The simplest way to characterize the variability in a texture pattern is by noting the gray-level values of the raw pixels. This set of gray values becomes the feature set on which the classification is based. An ... See full document

... Abstract—Support vector (SV) machines are linear classifiers that use the maximum margin hyperplane in a feature space defined by a kernel ...SV machines (within Valiant’s probably ... See full document

... We apply Support Vector Machines (SVMs) to identify English base phrases (chunks). SVMs are known to achieve high generalization perfor- mance even with input data of high dimensional feature spaces. ... See full document

... Support vector machines (SVMs) construct decision functions that are linear combinations of kernel evaluations on the training set. The samples with non-vanishing coefficients are called ... See full document

... We have presented a feasibility macromodel, which can be used during synthesis of analog circuits. The generated model, incorporating the proposed kernels has been found to be much more efficient while computing the ... See full document

... Abstract— In the cloud computing, security mechanisms are not mature enough to protect the data stored in the cloud. Hence, it is necessary to propose efficient methods for providing security to the data stored in the ... See full document