Support Vector Machines-Based Quantitative Structure−Property Relationship for the Prediction of Heat Capacity
نویسندگان
چکیده
منابع مشابه
Support Vector Machines-Based Quantitative Structure-Property Relationship for the Prediction of Heat Capacity
The support vector machine (SVM), as a novel type of learning machine, for the first time, was used to develop a Quantitative Structure-Property Relationship (QSPR) model of the heat capacity of a diverse set of 182 compounds based on the molecular descriptors calculated from the structure alone. Multiple linear regression (MLR) and radial basis function networks (RBFNNs) were also utilized to ...
متن کاملSupport Vector Machines for Differential Prediction
Machine learning is continually being applied to a growing set of fields, including the social sciences, business, and medicine. Some fields present problems that are not easily addressed using standard machine learning approaches and, in particular, there is growing interest in differential prediction. In this type of task we are interested in producing a classifier that specifically character...
متن کاملSupport vector machines for spatiotemporal tornado prediction
Support Vector Machines for Spatiotemporal Tornado Prediction INDRA ADRIANTO, THEODORE B. TRAFALIS, and VALLIAPPA LAKSHMANAN School of Industrial Engineering, University of Oklahoma, 202 West Boyd, Room 124, Norman, OK 73019, USA Phone: (405) 325-3721, Fax: (405) 325-7555 Emails: [email protected]; [email protected] Cooperative Institute of Mesoscale Meteorological Studies (CIMMS) University of Ok...
متن کاملHash-Based Support Vector Machines Approximation for Large Scale Prediction
How-to train effective classifiers on huge amount of multimedia data is clearly a major challenge that is attracting more and more research works across several communities. Less efforts however are spent on the counterpart scalability issue: how to apply big trained models efficiently on huge non annotated media collections ? In this paper, we address the problem of speeding-up the prediction ...
متن کاملA Comparative Study of Extreme Learning Machines and Support Vector Machines in Prediction of Sediment Transport in Open Channels
The limiting velocity in open channels to prevent long-term sedimentation is predicted in this paper using a powerful soft computing technique known as Extreme Learning Machines (ELM). The ELM is a single Layer Feed-forward Neural Network (SLFNN) with a high level of training speed. The dimensionless parameter of limiting velocity which is known as the densimetric Froude number (Fr) is predicte...
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: Journal of Chemical Information and Computer Sciences
سال: 2004
ISSN: 0095-2338
DOI: 10.1021/ci049934n