Pattern recognition is the process of recognizing patterns by using a machine learning algorithm ¹. It can be defined as the classification of data based on knowledge already gained or on statistical information extracted from patterns and/or their representation ¹. Pattern recognition involves the classification and cluster of patterns. In classification, an appropriate class label is assigned to a pattern based on an abstraction that is generated using a set of training patterns or domain knowledge. Classification is used in supervised learning. Clustering generated a partition of the data which helps decision making, the specific decision-making activity of interest to us. Clustering is used in unsupervised learning ¹.
Some examples of pattern recognition applications include speech recognition, speaker identification, multimedia document recognition (MDR), and automatic medical diagnosis ¹. In a typical pattern recognition application, the raw data is processed and converted into a form that is amenable for a machine to use ¹.
Features may be represented as continuous, discrete, or discrete binary variables. A feature is a function of one or more measurements, computed so that it quantifies some significant characteristics of the object ¹. For instance, in the case of speech, MFCC (Mel-frequency Cepstral Coefficient) is the spectral feature of the speech. The sequence of the first 13 features forms a feature vector ¹.
Training and learning are essential phases in pattern recognition. Learning is a phenomenon through which a system gets trained and becomes adaptable to give results accurately. The entire dataset is divided into two categories: one that is used in training the model i.e., Training set, and the other that is used in testing the model after training, i.e., Testing set ¹.
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pattern recognition is a data analysis method that uses machine learning algorithms to automatically recognise patterns and regularities in data
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Pattern recognition is the process of recognizing patterns by using a machine learning algorithm ¹. It can be defined as the classification of data based on knowledge already gained or on statistical information extracted from patterns and/or their representation ¹. Pattern recognition involves the classification and cluster of patterns. In classification, an appropriate class label is assigned to a pattern based on an abstraction that is generated using a set of training patterns or domain knowledge. Classification is used in supervised learning. Clustering generated a partition of the data which helps decision making, the specific decision-making activity of interest to us. Clustering is used in unsupervised learning ¹.
Some examples of pattern recognition applications include speech recognition, speaker identification, multimedia document recognition (MDR), and automatic medical diagnosis ¹. In a typical pattern recognition application, the raw data is processed and converted into a form that is amenable for a machine to use ¹.
Features may be represented as continuous, discrete, or discrete binary variables. A feature is a function of one or more measurements, computed so that it quantifies some significant characteristics of the object ¹. For instance, in the case of speech, MFCC (Mel-frequency Cepstral Coefficient) is the spectral feature of the speech. The sequence of the first 13 features forms a feature vector ¹.
Training and learning are essential phases in pattern recognition. Learning is a phenomenon through which a system gets trained and becomes adaptable to give results accurately. The entire dataset is divided into two categories: one that is used in training the model i.e., Training set, and the other that is used in testing the model after training, i.e., Testing set ¹.