What is Pattern Recognition?
Pattern recognition is a technique to classify input data into classes or objects by recognizing patterns or feature similarities. Unlike pattern matching which searches for exact matches, pattern recognition looks for a “most likely” pattern to classify all information provided. This can be done in a supervised (labeled data) learning model or unsupervised (unlabeled data) to discover new, hidden patterns.
How does Pattern Recognition Work?
The patterns are made up of individual features, which can be continuous, discrete or even discrete binary variables, or sets of features evaluated together, known as a feature vector. The biggest advantages are that this model will generate a classification of some confidence level for every data point and often reveals subtle, hidden patterns not readily seen with human intuition. Generally, the more feature variables the algorithm is programmed to check for and the more data points available for training, the more accurate it will be. This applies whether the database is labeled or unlabeled.
Which leads to the biggest disadvantages, that extra large datasets are required to generate high probability results, which makes the training time relatively slow and computationally expensive.