Research for SVM Classification using MATLAB at QUAD SOFTWARES Vadapalani - Chennai
Friday, 22 July 2016
Item details
City:
Chennai, Tamil Nadu
Offer type:
Offer
Item description
Research for SVM Classification using MATLAB at QUAD SOFTWARES Vadapalani
SVM is a useful technique for data classification. Even though it’s considered that Neural Networks are easier to use than this, however, sometimes unsatisfactory results are obtained. A classification task usually involves with training and testing data which consist of some data instances. Each instance in the training set contains one target values and several attributes. The goal of SVM is to produce a model which predicts target value of data instances in the testing set which are given only the attributes.
Classification in SVM is an example of Supervised Learning. Known labels help indicate whether the system is performing in a right way or not. This information points to a desired response, validating the accuracy of the system, or be used to help the system learn to act correctly. A step in SVM classification involves identification as which are intimately connected to the known classes. This is called feature selection or feature extraction. Feature selection and SVM classification together have a use even when prediction of unknown samples is not necessary. They can be used to identify key sets which are involved in whatever processes distinguish the classes.
For more information Pls contact us:
With Regards,
N. Mala
QUAD SOFTWARES,
OLD NO.46/1, NEW NO.122,
PILLAYAR KOIL STREET,
PANDURANGAN COLONY,
VADAPALANI, CHENNAI-600026.
LANDMARK: NEAR VADAPALANI MURUGAN TEMPLE
MOBILE NO: - 89390 01888, 89390 01666, 98844 81399
PHONE NO: - 044-42618280
SVM is a useful technique for data classification. Even though it’s considered that Neural Networks are easier to use than this, however, sometimes unsatisfactory results are obtained. A classification task usually involves with training and testing data which consist of some data instances. Each instance in the training set contains one target values and several attributes. The goal of SVM is to produce a model which predicts target value of data instances in the testing set which are given only the attributes.
Classification in SVM is an example of Supervised Learning. Known labels help indicate whether the system is performing in a right way or not. This information points to a desired response, validating the accuracy of the system, or be used to help the system learn to act correctly. A step in SVM classification involves identification as which are intimately connected to the known classes. This is called feature selection or feature extraction. Feature selection and SVM classification together have a use even when prediction of unknown samples is not necessary. They can be used to identify key sets which are involved in whatever processes distinguish the classes.
For more information Pls contact us:
With Regards,
N. Mala
QUAD SOFTWARES,
OLD NO.46/1, NEW NO.122,
PILLAYAR KOIL STREET,
PANDURANGAN COLONY,
VADAPALANI, CHENNAI-600026.
LANDMARK: NEAR VADAPALANI MURUGAN TEMPLE
MOBILE NO: - 89390 01888, 89390 01666, 98844 81399
PHONE NO: - 044-42618280
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