INSTANCE BASED MACHINE LEARNING

What is Machine Learning?

Machine learning is an application of artificial intelligence that generates programs for computer to access data and analyze it. Based on this analysis, the system automatically learns and improvises itself. Machine Learning is the study of certain set of rules using statistical and logical approach to solve a particular problem. These set of rules are called Machine Learning Algorithms and are used in machine learning for continuous development and improvement of specific task. Main aim of machine learning is to make machines learn automatically and work without human interference and assistance.

 Machine Learning is an approach to make life easier for human beings by reducing their efforts.
Face Recognition, virtual personal assistants like amazon’s alexa and Google home, filtering spam emails and malwares, online customer support, search engine result refining, social media services are a few applications of machine learning.

Types of Machine Learning Algorithms:

Based on the purpose they serve machine learning algorithms can be classified as:

• Supervised Learning
• Unsupervised Learning
• Reinforcement Learning

Instance Based Machine Learning Algorithm:

Also known as Memory based learning, Instance based learning is a supervised classification learning algorithm that performs operation after comparing the current instances with the previously trained instances, which have been stored in memory. Its name is derived from the fact that it creates assumption from the training data instances.

Time complexity of Instance based learning algorithm depends upon the size of training data. Time complexity of this algorithm in worst case is O (n), where n is the number of training items to be used to classify a single new instance.

To improve the efficiency of instance based learning approach, preprocessing phase is required. Preprocessing phase is a data structure that enables efficient usage of run time modeling of test instance.

Advantage of using Instance based learning over others is that it has the ability to adapt to previously unseen data, which means that one can store a new instance or drop the old instance.
Various instance based learning algorithms:

• K Nearest Neighbor (KNN)
• Self-Organizing Map (SOM)
• Learning Vector Quantization (LVQ)

K-Nearest Neighbor:

K nearest neighbors of a classifier are used to create a test instance model.

Example using KNN neighbor:
1.Splitting data into test and training datasets


2.Building and training model with training data

3. Evaluating model’s predictions against the test dataset

Self-Organizing Map:

SOM is a type of artificial neural network (ANN) that is used for producing map (i.e. low dimensional, different representation of input data).

Unlike other artificial neural networks, SOM applies competitive learning (i.e. learning in which nodes compete for the right to respond to a subset of the input data).
Operations performed in SOM:

• Select random input
• Compute winner neuron
• Update neurons
• Repeat for all input data
• Classify input data

SOM is used for dimensionality reduction (i.e. reduction of random variables in account by acquiring a set of principal variables).



Learning Vector Quantization:

Learning Vector Quantization is a competitive network using supervised learning. It is a process for classification of pattern in such a way that each output represents a class.