What is the difference between supervised learning and unsupervised learning? [closed]

Answer 1

Supervised learning can label a new item into one of the trained labels based on learning during training. You need to provide large numbers of training data set, validation data set and test data set. If you provide say pixel image vectors of digits along with training data with labels, then it can identify the numbers.

Unsupervised learning does not require training data-sets. In unsupervised learning it can group items into different clusters based on the difference in the input vectors. If you provide pixel image vectors of digits and ask it to classify into 10 categories, it may do that. But it does know how to labels it as you have not provided training labels.

Answer 2

Machine learning is a field where you are trying to make machine to mimic the human behavior.

You train machine just like a baby.The way humans learn, identify features, recognize patterns and train himself, same way you train machine by feeding data with various features. Machine algorithm identify the pattern within the data and classify it into particular category.

Machine learning broadly divided into two category, supervised and unsupervised learning.

Supervised learning is the concept where you have input vector / data with corresponding target value (output).On the other hand unsupervised learning is the concept where you only have input vectors / data without any corresponding target value.

An example of supervised learning is handwritten digits recognition where you have image of digits with corresponding digit [0-9], and an example of unsupervised learning is grouping customers by purchasing behavior.

Answer 3

For instance, very often training a neural network is supervised learning: you're telling the network to which class corresponds the feature vector you're feeding.

Clustering is unsupervised learning: you let the algorithm decide how to group samples into classes that share common properties.

Another example of unsupervised learning is Kohonen's self organizing maps.

Answer 4

Supervised Learning

In this, every input pattern that is used to train the network is associated with an output pattern, which is the target or the desired pattern. A teacher is assumed to be present during the learning process, when a comparison is made between the network's computed output and the correct expected output, to determine the error. The error can then be used to change network parameters, which result in an improvement in performance.

Unsupervised Learning

In this learning method, the target output is not presented to the network. It is as if there is no teacher to present the desired pattern and hence, the system learns of its own by discovering and adapting to structural features in the input patterns.

Answer 5

In Simple Supervised learning is type of machine learning problem in which we have some labels and by using that labels we implement algorithm such as regression and classification .Classification is applied where our output is like in the form of 0 or 1 ,true/false,yes/no. and regression is applied where out put a real value such a house of price

Unsupervised Learning is a type of machine learning problem in which we don't have any labels means we have some data only ,unstructured data and we have to cluster the data (grouping of data)using various unsupervised algorithm