Hidden Space Support Vector Machines

Hidden 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...

Version Space Support Vector Machines

The task of reliable classification is to determine if a particular instance classification is reliable. There exist two approaches to the task: the Bayesian framework [3] and the typicalness framework [5]. Although both frameworks are useful, the Bayesian framework can be misleading and the typicalness framework is classifier dependent. To overcome these problems we argue to use version spaces...

STAGE-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

Hidden Space Smooth Support Vector Machine with C Means Clustering

Co-Training of Version Space Support Vector Machines

One of the main problems when machine-learning classifiers are employed in practice is to determine whether classifications assigned to new instances are reliable. Recently, version spaces were proposed to be considered as an approach to reliable instance classification. The unanimous-voting classification rule of version spaces does not misclassify, i.e., instance classifications become reliab...