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I will pay for the following article Error-Correcting Output Codes

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I will pay for the following article Error-Correcting Output Codes. The work is to be 9 pages with three to five sources, with in-text citations and a reference page. Moreover, its ability to reinforce classification accurately utilizes complex problems based on outputs. Therefore, complex problems can be fixed using ECOC by placing classification accurately and producing positive outputs of data. The classification of data requires proper use of ECOC, so that complex problem can be fixed effectively especially in structures- structured output prediction (Ngoc, Chong& Adam, 2011). Besides, the main motivation for using ECOC in computer science is to develop new structured methods for structured output prediction and the concept of multiple classifier systems (MCS). The development of new structured methods and structured output prediction assists in giving output data that is accurate and reliable. Moreover, the prediction enables data users to have prior knowledge of the figures they need to expect in the final output results. This enables for quality results of final data and data recognition that allows for an effective data classification of projects and general easy solving of complex problems. The concept of multiple classifiers systems is made easier using ECOC (Bhardwaj & Jain, 2010). The systems are easy to use in the computers through the implementation of ECOC in the development of multiple classifier systems. Therefore, Error-Correcting Output Codes has been an essential computer technology and information development, since it is used in many applications for multiple data classification and designing of combined classifiers for effective data recognition. Consequently, varied problems in pattern recognition can be solved through the use of ECOC, which give quality results of error correction by the use of the multiple classifiers systems (Assuncao & Costanazo, 2009).

Error-Correcting Output Codes were initiated in the field of pattern recognition for solving the tribulations of multiple classes. The main idea for this development was to ignore solving multi-class problems openly but decompose into dichotomies.&nbsp.