The importance of pattern recognition in machine learning is to identify and predict even the untraceable or small hidden data and recognize objects at varying distances to classify them into various categories. It classifies based on the statistical information or historical data from the pattern and their representation. Pattern recognition refers to the recognizing patterns and regularities of data. Pattern recognition has several application areas, such as statistical data analysis, signal processing, image analysis, information retrieval, bioinformatics, data compression, computer graphics, and machine learning. In machine learning, Pattern recognition involves two algorithm approaches, Supervised learning algorithms: This classification algorithm involves two processes to identify patterns such as building a model and predicting the new or unseen data, Naive Bayes, Decision Tree, Support Vector Machines are some supervised learning algorithms.
Unsupervised learning algorithms: This approach group the observed data based on the similarity in features; k-means, Mean Shift, DBSCAN are the few unsupervised algorithms. Stages in pattern recognition are input sensing, segmentation, feature extraction, classification, post-processing, and decision making. The most common application of pattern recognition are Trend analysis - Based on the recent trends in the sales of a particular organization, future sales are predicted, Biometric devices - Face and fingerprint recognition, Computer vision - Cancer detection, E-commerce- Online shopping websites. Advanced research areas of pattern recognition are IoT Networks and also Traffic Patterns Pattern Recognition for Agriculture, Temporal Pattern Mining, Mining Medical data, Character and also Text Recognition, Gesture and also Behavior Analysis, Handwriting Recognition, Traffic Analysis and also Control, Graphics Recognition, and Multi-Bioinformatics.
• Pattern recognition is concerned with analyzing the scene in the real world and designing and developing systems that recognize patterns in data.
• Classification and Recognition performance has a surprising improvement on the prior-art configurations but increases the network depth.
• Machine learning in pattern recognition involving as a recognition and classification task through supervised and unsupervised learning algorithms.
• Machine learning methods analyze high dimensional data with unknown statistical characteristics by learning the model structure directly from training data and improving the recognition rate or accuracy.
• Machine learning is actively used in artificial neural networks to identify fingerprints, computer-aided diagnosis, speech recognition, face detection, verify the signature, or complete the authentication of a voiced-based procedure.
• In order to achieve better accuracy in both recognition and classification tasks in various application domains, an adaptive or hybrid machine learning technique is necessary for pattern recognition.