Support Vector Machines (SVM) as a Technique for Solvency Analysis
نویسندگان
چکیده
منابع مشابه
Parallel Support Vector Machines: The Cascade SVM
We describe an algorithm for support vector machines (SVM) that can be parallelized efficiently and scales to very large problems with hundreds of thousands of training vectors. Instead of analyzing the whole training set in one optimization step, the data are split into subsets and optimized separately with multiple SVMs. The partial results are combined and filtered again in a ‘Cascade’ of SV...
متن کاملSVM Categorizer: A Generic Categorization Tool Using Support Vector Machines
Supervised text categorisation is a significant tool considering the vast amount of structured, unstru ctured, or semi-structured texts that are available from internal or external enterprise resources. The goal of supervised text categorisation is to assign text documents to finite pre -specified categories in order to extract and automatically organise information coming from th ese resources...
متن کاملMining Biological Repetitive Sequences Using Support Vector Machines and Fuzzy SVM
Structural repetitive subsequences are most important portion of biological sequences, which play crucial roles on corresponding sequence’s fold and functionality. Biggest class of the repetitive subsequences is “Transposable Elements” which has its own sub-classes upon contexts’ structures. Many researches have been performed to criticality determine the structure and function of repetitiv...
متن کاملSTAGE-DISCHARGE MODELING USING SUPPORT VECTOR MACHINES
Establishment of rating curves are often required by the hydrologists for flow estimates in the streams, rivers etc. Measurement of discharge in a river is a time-consuming, expensive, and difficult process and the conventional approach of regression analysis of stage-discharge relation does not provide encouraging results especially during the floods. P
متن کاملA Random Sampling Technique for Training Support Vector Machines
Random sampling techniques have been developed for combinatorial optimization problems. In this note, we report an application of one of these techniques for training support vector machines (more precisely, primal-form maximal-margin classifiers) that solve two-group classification problems by using hyperplane classifiers. Through this research, we are aiming (I) to design efficient and theore...
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: SSRN Electronic Journal
سال: 2008
ISSN: 1556-5068
DOI: 10.2139/ssrn.1424949