SSDA [unit 5]

Learning Tools, Techniques and Applications.

1. Inductive Learning

This involves the process of learning by example -- where a system tries to induce a general rule

from a set of observed instances. This involves classification -- assigning, to a particular input,

the name of a class to which it belongs. Classification is important to many problem solving

tasks. A learning system has to be capable of evolving its own class descriptions:

Initial class definitions may not be adequate.

The world may not be well understood or rapidly changing. The task of constructing class

definitions is called induction or concept learning.

Fig. induction model.

2. Decision tree learning:

Decision tree learning uses a decision tree as a predictive model which maps

observations about an item (represented in the branches) to conclusions about the item's

target value (represented in the leaves). It is one of the predictive modelling approaches

used in statistics, data mining and machine learning. Tree models where the target

variable can take a finite set of values are called classification trees; in these tree

structures, leaves represent class labels and branches represent conjunctions of features

that lead to those class labels. Decision trees where the target variable can take

continuous values (typically real numbers) are called regression trees. In decision

analysis, a decision tree can be used to visually and explicitly represent decisions and

decision making. In data mining, a decision tree describes data (but the resulting

classification tree can be an input for decision making). This page deals with decision

trees in data mining.

Decision trees used in data mining are of two main types:

Classification tree analysis is when the predicted outcome is the class to which the data

belongs.

Regression tree analysis is when the predicted outcome can be considered a real number

(e.g. the price of a house, or a patient’s length of stay in a hospital).

The term Classification And Regression Tree (CART) analysis is an umbrella term used to

refer to both of the above procedures, first introduced by Breiman et al.[3] Trees used for

regression and trees used for classification have some similarities - but also some differences,

such as the procedure used to determine where to split.[3]

Some techniques, often called ensemble methods, construct more than one decision tree:

Bagging decision trees, an early ensemble method, builds multiple decision trees by

repeatedly resampling training data with replacement, and voting the trees for a

consensus prediction.[4]

A Random Forest classifier uses a number of decision trees in order to improve the

classification rate.

3. Explanation based learning:

Explanation-based learning (EBL) is a form of machine learning that exploits a very

strong, or even perfect, domain theory to make generalizations or form concepts from

training examples.

4. Non parametric model:

5. Artificial Neural Network :

An artificial neuron network (ANN) is a computational model based on the structure and

functions of biological neural networks. Information that flows through the network

affects the structure of the ANN because a neural network changes - or learns, in a sense -

based on that input and output.

ANNs are considered nonlinear statistical data modeling tools where the complex

relationships between inputs and outputs are modeled or patterns are found.

ANN is also known as a neural network.

6. Reinforcement Learning:

7. Active Learning:

8. Empirical Learning:

Empirical research is research using empirical evidence. It is a way of gaining

knowledge by means of direct and indirect observation or experience. Empiricism values

such research more than other kinds. ... Usually, a researcher has a certain theory

regarding the topic under investigation.

9. Ensemble Learning:

Ensemble learning is a machine learning paradigm where multiple learners are trained to

solve the same problem. In contrast to ordinary machine learning approaches which try to

learn one hypothesis from training data, ensemble methods try to construct a set of

hypotheses and combine them to use.

10. Support Vector Machines:

A Support Vector Machine (SVM) is a discriminative classifier formally defined by a

separating hyperplane. In other words, given labeled training data (supervised learning), the

algorithm outputs an optimal hyperplane which categorizes new examples.

11. Machine Learning :

Machine learning is a type of artificial intelligence (AI) that provides computers with

the ability to learn without being explicitly programmed. Machine learning focuses on

the development of computer programs that can teach themselves to grow and change

when exposed to new data.

12. Supervised learning :

13. Semi-supervised learning:

Semi-supervised learning is a class of supervised learning tasks and techniques that also make

use of unlabeled data for training – typically a small amount of labeled data with a large amount

of unlabeled data.