Support Vector Machine Training for Improved Hidden Markov Modeling
نویسندگان
چکیده
منابع مشابه
Least Squares Support Vector Machine for Constitutive Modeling of Clay
Constitutive modeling of clay is an important research in geotechnical engineering. It is difficult to use precise mathematical expressions to approximate stress-strain relationship of clay. Artificial neural network (ANN) and support vector machine (SVM) have been successfully used in constitutive modeling of clay. However, generalization ability of ANN has some limitations, and application of...
متن کاملAudio classification by hybrid support vector machine / hidden Markov model *
Audio is one of important information carriers in the multimedia. It contains abundant semantics and enriches information perception and acquisition. At present, it always uses vision information in the multimedia retrieval, but ignores audio information. In this paper, the problem of audio classification is discussed. The combination of Support Vector Machine and Hidden Markov Model is describ...
متن کاملAccent Classification Using Support Vector Machine and Hidden Markov Model
Accent classification technologies directly influence the performance of speech recognition. Currently, two models are used for accent detection namely: Hidden Markov Model (HMM) and Artificial Neural Networks (ANN). However, both models have some drawbacks of their own. In this paper, we use Support Vector Machine (SVM) to detect different speakers’ accents. To examine the performance of SVM, ...
متن کاملHidden Markov Support Vector Machines
This paper presents a novel discriminative learning technique for label sequences based on a combination of the two most successful learning algorithms, Support Vector Machines and Hidden Markov Models which we call Hidden Markov Support Vector Machine. The proposed architecture handles dependencies between neighboring labels using Viterbi decoding. In contrast to standard HMM training, the lea...
متن کاملUsing Wavelet Support Vector Machine for Fault Diagnosis of Gearboxes
Identifying fault categories, especially for compound faults, is a challenging task in mechanical fault diagnosis. For this task, this paper proposes a novel intelligent method based on wavelet packet transform (WPT) and multiple classifier fusion. An unexpected damage on the gearbox may break the whole transmission line down. It is therefore crucial for engineers and researchers to monitor the...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: IEEE Transactions on Signal Processing
سال: 2008
ISSN: 1053-587X
DOI: 10.1109/tsp.2007.906741