What is the difference between supervised learning and unsupervised learning?

问题:

In terms of artificial intelligence and machine learning. Can you provide a basic, easy explanation with an example?

回答1:

Since you ask this very basic question, it looks like it's worth specifying what Machine Learning itself is.

Machine Learning is a class of algorithms which is data-driven, i.e. unlike "normal" algorithms it is the data that "tells" what the "good answer" is. Example: a hypothetical non-machine learning algorithm for face detection in images would try to define what a face is (round skin-like-colored disk, with dark area where you expect the eyes etc). A machine learning algorithm would not have such coded definition, but would "learn-by-examples": you'll show several images of faces and not-faces and a good algorithm will eventually learn and be able to predict whether or not an unseen image is a face.

This particular example of face detection is supervised, which means that your examples must be labeled, or explicitly say which ones are faces and which ones aren't.

In an unsupervised algorithm your examples are not labeled, i.e. you don't say anything. Of course, in such a case the algorithm itself cannot "invent" what a face is, but it can try to cluster the data into different groups, e.g. it can distinguish that faces are very different from landscapes, which are very different from horses.

Since another answer mentions it (though, in an incorrect way): there are "intermediate" forms of supervision, i.e. semi-supervised and active learning. Technically, these are supervised methods in which there is some "smart" way to avoid a large number of labeled examples. In active learning, the algorithm itself decides which thing you should label (e.g. it can be pretty sure about a landscape and a horse, but it might ask you to confirm if a gorilla is indeed the picture of a face). In semi-supervised learning, there are two different algorithms which start with the labeled examples, and then "tell" each other the way they think about some large number of unlabeled data. From this "discussion" they learn.

回答2:

Supervised learning is when the data you feed your algorithm is "tagged" to help your logic make decisions.

Example: Bayes spam filtering, where you have to flag an item as spam to refine the results.

Unsupervised learning are types of algorithms that try to find correlations without any external inputs other than the raw data.

Example: datamining clustering algorithms.

回答3:

Supervised learning

Applications in which the training data comprises examples of the input vectors along with their corresponding target vectors are known as supervised learning problems.

Unsupervised learning

In other pattern recognition problems, the training data consists of a set of input vectors x without any corresponding target values. The goal in such unsupervised learning problems may be to discover groups of similar examples within the data, where it is called clustering

Pattern Recognition and Machine Learning (Bishop, 2006)

回答4:

In supervised learning, scheme is being provided with actual outcome.So outcome is called class of the example.

But in unsupervised learning, class of an example is not known.So unsupervised learning is that of trying to find hidden structure in unlabeled(unknown class) data set.

Approaches to unsupervised learning include:

• Clustering(K-mean,hierarchical clustering)

• Association Rule Learning

Approaches to supervised learning include:

• Classification(1R, Naive Bayes, Decision tree learning algorithm such as ID3 CART and so on)

• Numeric Value Prediction

回答5:

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.

回答6:

If you need to know the meaning of them so simply,I can tell you an example: as an example: you need to recognize which vehicle is car and which one is motorcycle? in supervised learning your input data should have label.It means that first you should assign that the vehicle which has 2 wheels and size is small is motorcycle.(In this case we give the information directly). But in unsupervised learning, you don't label the inputs.you give some different inputs to machine and the machine cluster them by their similar features.

回答7:

Short answer:

Machine learning can be divided into two learning algorithms:

Supervised Learning: You give variously labelled example data as input, along with the correct answers. This algorithm will learn from it, and start predicting correct results based on the inputs thereafter. Example: Email Spam filter

Unsupervised Learning: You just give data and don't tell anything - like labels or correct answers. Algorithm automatically analyses patterns in the data. Example: Google News

回答8:

I have always found the distinction between unsupervised and supervised learning to be arbitrary and a little confusing. There is no real distinction between the two cases, instead there is a range of situations in which an algorithm can have more or less 'supervision'. The existence of semi-supervised learning is an obvious examples where the line is blurred.

I tend to think of supervision as giving feedback to the algorithm about what solutions should be preferred. For a traditional supervised setting, such as spam detection, you tell the algorithm "don't make any mistakes on the training set"; for a traditional unsupervised setting, such as clustering, you tell the algorithm "points that are close to each other should be in the same cluster". It just so happens that, the first form of feedback is a lot more specific than the latter.

In short, when someone says 'supervised', think classification, when they say 'unsupervised' think clustering and try not to worry too much about it beyond that.

回答9:

Machine learning: It explores the study and construction of algorithms that can learn from and make predictions on data.Such algorithms operate by building a model from example inputs in order to make data-driven predictions or decisions expressed as outputs,rather than following strictly static program instructions.

Supervised learning: It is the machine learning task of inferring a function from labeled 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 can be used for mapping new examples.

The computer is presented with example inputs and their desired outputs, given by a "teacher", and the goal is to learn a general rule that maps inputs to outputs.Specifically, a supervised learning algorithm takes a known set of input data and known responses to the data (output), and trains a model to generate reasonable predictions for the response to new data.

Unsupervised learning: It is learning without a teacher. One basic thing that you might want to do with data is to visualize it. It is the machine learning task of inferring a function to describe hidden structure from unlabeled data. Since the examples given to the learner are unlabeled, there is no error or reward signal to evaluate a potential solution. This distinguishes unsupervised learning from supervised learning. Unsupervised learning uses procedures that attempt to find natural partitions of patterns.

With unsupervised learning there is no feedback based on the prediction results, i.e., there is no teacher to correct you.Under the Unsupervised learning methods no labeled examples are provided and there is no notion of the output during the learning process. As a result, it is up to the learning scheme/model to find patterns or discover the groups of the input data

You should use unsupervised learning methods when you need a large amount of data to train your models, and the willingness and ability to experiment and explore, and of course a challenge that isn’t well solved via more-established methods.With unsupervised learning it is possible to learn larger and more complex models than with supervised learning.Here is a good example on it

.

回答10:

Supervised learning, given the data with an answer.

Given email labeled as spam/not spam, learn a spam filter.

Given a dataset of patients diagnosed as either having diabetes or not, learn to classify new patients as having diabetes or not.

Unsupervised learning, given the data without an answer, let the pc to group things.

Given a set of news articles found on the web, group the into set of articles about the same story.

Given a database of custom data, automatically discover market segments and group customers into different market segments.

Reference

回答11:

Supervised Learning: It is a machine learning task of inferring a function from labeled training data.

Unsupervised Learning: It is a machine learning task of inferring a function to describe hidden structure from "unlabeled" data (a classification or categorization is not included in the observations).

回答12:

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.

回答13:

I'll try to keep it simple.

Supervised Learning: In this technique of learning we are given a data set and the system already knows the corrects output of the dataset. So here our system learn by predicting a value of its own. Then it does a accuracy check by using a cost function, to check how close his prediction was to actual output.

Unsupervised Learning: In this we approach with little or No knowledge of what our result would be. So instead we derive structure from the data where we don't know effect of variable. We make structure by clustering the data based on relationship among the variable in data. Here we don't have a feedback based on our prediction.

回答14:

Supervised Learning

Supervised learning is based on training a data sample from data source with correct classification already assigned. Such techniques are utilized in feedforward or MultiLayer Perceptron (MLP) models. These MLP has three distinctive characteristics:

1. One or more layers of hidden neurons that are not part of the input or output layers of the network that enable the network to learn and solve any complex problems
2. The nonlinearity reflected in the neuronal activity is differentiable and,
3. The interconnection model of the network exhibits a high degree of connectivity.

These characteristics along with learning through training solve difficult and diverse problems. Learning through training in a supervised ANN model also called as error backpropagation algorithm. The error correction-learning algorithm trains the network based on the input-output samples and finds error signal, which is the difference of the output calculated and the desired output and adjusts the synaptic weights of the neurons that is proportional to the product of the error signal and the input instance of the synaptic weight. Based on this principle, error back propagation learning occurs in two passes:

Forward Pass:

Here, input vector is presented to the network. This input signal propagates forward, neuron by neuron through the network and emerges at the output end of the network as output signal: y(n) = φ(v(n)) where v(n) is the induced local field of a neuron defined by v(n) =∑ w(n)y(n). The output that is calculated at the output layer o(n) is compared with the desired response d(n) and finds the error e(n) for that neuron. The synaptic weights of the network during this pass are remains same.

Backward Pass:

The error signal that is originated at the output neuron of that layer is propagated backward through network. This calculates the local gradient for each neuron in each layer and allows the synaptic weights of the network to undergo changes in accordance with the delta rule as:

Δw(n) = η * δ(n) * y(n). 

This recursive computation is continued, with forward pass followed by the backward pass for each input pattern till the network is converged.

Supervised learning paradigm of an ANN is efficient and finds solutions to several linear and non-linear problems such as classification, plant control, forecasting, prediction, robotics etc.

Unsupervised Learning

Self-Organizing neural networks learn using unsupervised learning algorithm to identify hidden patterns in unlabelled input data. This unsupervised refers to the ability to learn and organize information without providing an error signal to evaluate the potential solution. The lack of direction for the learning algorithm in unsupervised learning can sometime be advantageous, since it lets the algorithm to look back for patterns that have not been previously considered. The main characteristics of Self-Organizing Maps (SOM) are:

1. It transforms an incoming signal pattern of arbitrary dimension into one or 2 dimensional map and perform this transformation adaptively
2. The network represents feedforward structure with a single computational layer consisting of neurons arranged in rows and columns. At each stage of representation, each input signal is kept in its proper context and,
3. Neurons dealing with closely related pieces of information are close together and they communicate through synaptic connections.

The computational layer is also called as competitive layer since the neurons in the layer compete with each other to become active. Hence, this learning algorithm is called competitive algorithm. Unsupervised algorithm in SOM works in three phases:

Competition phase:

for each input pattern x, presented to the network, inner product with synaptic weight w is calculated and the neurons in the competitive layer finds a discriminant function that induce competition among the neurons and the synaptic weight vector that is close to the input vector in the Euclidean distance is announced as winner in the competition. That neuron is called best matching neuron,

Cooperative phase:

the winning neuron determines the center of a topological neighborhood h of cooperating neurons. This is performed by the lateral interaction d among the cooperative neurons. This topological neighborhood reduces its size over a time period.

Adaptive phase:

enables the winning neuron and its neighborhood neurons to increase their individual values of the discriminant function in relation to the input pattern through suitable synaptic weight adjustments,

Upon repeated presentation of the training patterns, the synaptic weight vectors tend to follow the distribution of the input patterns due to the neighborhood updating and thus ANN learns without supervisor.

Self-Organizing Model naturally represents the neuro-biological behavior, and hence is used in many real world applications such as clustering, speech recognition, texture segmentation, vector coding etc.

Reference.

回答15:

In Supervised Learning we know what the input and output should be. For example , given a set of cars. We have to find out which ones red and which ones blue.

Whereas, Unsupervised learning is where we have to find out the answer with a very little or without any idea about how the output should be. For example, a learner might be able to build a model that detects when people are smiling based on correlation of facial patterns and words such as "what are you smiling about?".

回答16:

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.

回答17:

Supervised Learning is basically where you have input variables(x) and output variable(y) and use algorithm to learn the mapping function from input to the output. The reason why we called this as supervised is because algorithm learns from the training dataset, the algorithm iteratively makes predictions on the training data. Supervised have two types-Classification and Regression. Classification is when the output variable is category like yes/no, true/false. Regression is when the output is real values like height of person, Temperature etc.

UN supervised learning is where we have only input data(X) and no output variables. This is called an unsupervised learning because unlike supervised learning above there is no correct answers and there is no teacher. Algorithms are left to their own devises to discover and present the interesting structure in the data.

Types of unsupervised learning are clustering and Association.

回答18:

Supervised Machine Learning

"The process of an algorithm learning from training dataset and predict the output. "

Accuracy of predicted output directly proportional to the training data (length)

Supervised learning is where you have input variables (x) (training dataset)and an output variable (Y) (testing dataset) and you use an algorithm to learn the mapping function from the input to the output.

Y = f(X) 

Major types:

• Classification (discrete y-axis)

• Predictive (continuous y-axis)

Algorithms:

• Classification Algorithms:

  Predictive Algorithms:

  Nearest  neighbor   Linear Regression,Multi Regression  

Application areas:

• 1) Classifying emails as spam
  2) Classifying whether patient has disease or not
  3) Voice Recognition

  4) Predict the HR select particular candidate or not

  5) Predict the stock market price

回答19:

Supervised Learning is basically a technique in which the training data from which the machine learns is already labelled that is suppose a simple even odd number classifier where you have already classified the data during training . Therefore it uses "LABELLED" data.

Unsupervised learning on the contrary is a technique in which the machine by itself labels the data . Or you can say its the case when the machine learns by itself from scratch.

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