Smooth relevance vector machine: a smoothness prior extension of the RVM
نویسندگان
چکیده
منابع مشابه
Vector Machine : a smoothness prior extension of the RVM ( submitted draft version – do not quote )
Enforcing sparsity constraints has been shown to be an effective and efficient way to obtain state-of-the-art results in regression and classification tasks. Unlike the support vector machine (SVM) the relevance vector machine (RVM) explicitly encodes the criterion of model sparsity as a prior over the model weights. However the lack of an explicit prior structure over the weight variances mean...
متن کاملA Prior for Consistent Estimation for The Relevance Vector Machine
The Relevance Vector Machine (RVM) provides an empirical Bayes treatment of function approximation by kernel basis expansion. In its original form ?, RVM achieves a sparse representation of the approximating function by structuring a Gaussian prior distribution in a way that implicitly puts a sparsity pressure on the coefficients appearing in the expansion. RVM aims at retaining the tractabilit...
متن کاملA Survey related to Gene Selection and Cancer Classification using Relevance Vector Machine (RVM)
Now a day‘s cancer is the most dangerous diseases in the world. There were bunch of proposal from a variety of establishers and detailed picture examination was still under processing. Generally cancer is defined as the abnormal growth and uncontrolled growth in the human bodies. Many cells are constructed to form a living organisms like planets, humans and animals etc. Each cell containing one...
متن کاملThe Relevance Vector Machine
The support vector machine (SVM) is a state-of-the-art technique for regression and classification, combining excellent generalisation properties with a sparse kernel representation. However, it does suffer from a number of disadvantages, notably the absence of probabilistic outputs, the requirement to estimate a trade-off parameter and the need to utilise 'Mercer' kernel functions. In this pap...
متن کاملPerson Authentication using Relevance Vector Machine (RVM) for Face and Fingerprint
Multimodal biometric systems have proven more efficient in personal verification or identification than single biometric ones, so it is also a focus of this paper. Particularly, in the paper, the authors present a multimodal biometric system in which features from face and fingerprint images are extracted using Zernike Moment (ZM), the personal authentication is done using Relevance Vector Mach...
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: Machine Learning
سال: 2007
ISSN: 0885-6125,1573-0565
DOI: 10.1007/s10994-007-5012-z