[PDF] Top 20 Object Recognition System-on-Chip Using the Support Vector Machines

... a system-on-chip (SoC) platform dedicated to digital image and signal processing, which is tuned to implement efficiently multiply-and-accumulate (MAC) vector/matrix ...the support vector ... See full document

... Teller Machines (ATMs) enable customers to perform financial transactions like cash withdrawal, check balances or credit mobile phones with the help of the machine and without any human ...a chip and a ... See full document

... In this work, the method for extracting named entities from data of various domains has been presented which is a system useful in the identification and classification of names. The work for Nepali NER is very ... See full document

... three-stage support vector machine (SVM) for facial expression recognition is ...proposed system is competitive and has better performance compared with other ... See full document

... Entity Recognition using comparatively three different machine learning techniques: (i) Support Vector Machines (SVM), (ii) Maximum Entropy and (iii) Onetime, a shortcut method based on ... See full document

... information using a key ...Character recognition software which can recognize even remotely used Telugu ...by using Sobolev Coefficients which contains ordered sequences of coordinates created by ... See full document

... HMM) using 13 mel- frequency cepstral coefficients (MFCC) with the first and second derivatives achieves ...DBN-HMM system combining deep belief network and hidden Markov model achieves ...speaker ... See full document

... face recognition has attracted the in- terest of many researchers in later years mainly due to its applicability in security systems, surveillance and criminal ...face recognition is still considered a ... See full document

... and support vector machines ...trained using the Excel ...progressively using the back propagation algorithm in order to reduce the big difference between the calculated values and the ... 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 ... See full document

... It is well known that the performance of a digit- recognition system depends significantly on the features used. Selection of a feature extraction method is probably the single most important factor in ... See full document

... [13] recognition was based on the comparison of syllable ...[15] recognition of species that produce regularly inharmonic sounds were ...applying support vector machine classifiers to the ... See full document

... one in which a nearest neighbor classifier was utilized for classification. Li and Yin [79] presented a framework in which a face picture is initially deteriorated with a wavelet transform to three levels. The ... See full document

... Nogueira et al. (2008), using LDA, identified and evaluated new characteristics for production purposes and differentiation of soybean cultivars. They stated that these characteristics are useful as additional ... See full document

... Let’s consider the possibility of classifier performance improvement by the increase of the total number of training images per class from 400 to 5000. Experiments showed that SVM and KDDA recognition rates can’t ... See full document

... One of the approaches for corpus error de- tection is use of machine learning techniques (Abney et al., 1999; Matsumoto and Yamashita, 2000; Ma et al., 2001). These methods regard difficult elements for a learning model ... See full document

... original support vector machine (SVM) is a binary classification method, while the sentence reduction problem is formulated as multiple classification, we have to find a method to adapt support ... See full document

... Awaida, “Recognition Of Off-Line Handwritten Arabic Indian Numerals Using Multi-Scale Features And Support Vector Machines Vs.Hidden Markov Models” The Arabian Journal For Science And En[r] ... See full document

... Downs et al. (2001) proposed a technique which finds samples that are linearly dependent in the RKHS in order to construct representations that are more sparse than the ones found by optimizing the dual of the L1-SVM ... See full document

... This data set consists of 20 sections (02-21) of the WSJ part of the Penn Treebank for the training data, and one section (00) for the test data. POS tags in this data sets are also anno- tated by the Brill tagger. We ... See full document