Pattern Recognition Algorithms

Pattern Recognition Algorithms
For pattern recognition, various researches & various algorithms have been proposed. The types of pattern recognition are governed by its design cycle. As we know, it consists of basic elements like “visual perception”, “feature extraction” & “classification”. There are various different techniques and algorithms to implement these basic elements. So what technique is chosen for each element in design cycle tells the algorithm characteristic of pattern recognition.

Algorithms for pattern recognition depend on the type of label output, on whether learning is supervised or unsupervised, and whether the algorithm is statistical or non-statistical in nature. Statistical algorithm can be further categorized as generative or discriminative. Before going various types of algorithms, let first understand few definitions.

Statistical Classification: “Statistical classification is the problem of identifying the sub population to which new observation belong”

Supervised Learning: “Supervised learning is the machine learning task of inferring a function from supervised training data. The training data consist of a set of training examples. In supervised learning, each example is a pair consisting of an input object (typically a vector) and a desired output value (also called the supervisory signal). A supervised learning algorithm analyzes the training data and produces an inferred function, which is called a classifier”

Unsupervised Learning: “Un-supervised learning refers to the problem of trying to find hidden structure in unlabeled data. Since the examples given to the learner are unlabeled, there is no error or reward signal to evaluate a potential solution.”