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Questions tagged [unsupervised-learning]

Unsupervised learning refers to machine learning contexts in which there is no prior 'training' period in which the learning agent is trained on objects of known type. As such, supervised learning includes such disciplines as mathematical clustering, whereby data is segmented into clusters based on the minimisation or maximisation of mathematical properties and not on an attempt to classify by understanding the right context.

Unsupervised learning (or clustering) refers to machine learning algorithms in which there is no 'label' available for the training data and the model tries to learn the underlying manifold. As such, unsupervised learning includes such disciplines as mathematical clustering, whereby data is segmented into clusters based on the minimization or maximization of mathematical properties and not on an attempt to classify by understanding the right context.

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What is the difference between supervised learning and unsupervised learning?

In terms of artificial intelligence and machine learning, what is the difference between supervised and unsupervised learning? Can you provide a basic, easy explanation with an example?
TIMEX
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Can anyone give a real life example of supervised learning and unsupervised learning?

I recently studied about supervised learning and unsupervised learning. From theory, I know that supervised means getting the information from labeled datasets and unsupervised means clustering the data without any labels given. But, the problem is…
krupal
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Unsupervised pre-training for convolutional neural network in theano

I would like to design a deep net with one (or more) convolutional layers (CNN) and one or more fully connected hidden layers on top. For deep network with fully connected layers there are methods in theano for unsupervised pre-training, e.g., using…
Shai
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Genetic algorithm /w Neural Network playing snake is not improving

I am attempting to create a genetic algorithm to train a neural network, with the goal of playing the game snake. The problem I am having is that the fitness of the generations isn't improving, it either sits still at the fitness one could expect…
Ben
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What is the state-of-the-art in unsupervised learning on temporal data?

I'm looking for an overview of the state-of-the-art methods that find temporal patterns (of arbitrary length) in temporal data and are unsupervised (no labels). In other words, given a steam/sequence of (potentially high-dimensional) data, how do…
schaul
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How to extract unsupervised clusters from a Dirichlet Process in PyMC3?

I just finished the Bayesian Analysis in Python book by Osvaldo Martin (great book to understand bayesian concepts and some fancy numpy indexing). I really want to extend my understanding to bayesian mixture models for unsupervised clustering of…
O.rka
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what is distant supervision?

According to my understanding, Distant Supervision is the process of specifying the concept which the individual words of a passage, usually a sentence, are trying to convey. For example, a database maintains the structured relationship concerns(…
smatthewenglish
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Clustering of news articles

My scenario is pretty straightforwrd: I have a bunch of news articles (~1k at the moment) for which I know that some cover the same story/topic. I now would like to group these articles based on shared story/topic, i.e., based on their…
Christian
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How do you decide the parameters of a Convolutional Neural Network for image classification?

I am using Convolutional Neural Networks (Unsupervised Feature learning to detect features + Softmax Regression Classifier) for image classification. I have gone through all the tutorials by Andrew NG in this area.…
user3705926
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Understanding DynamicTreeCut algorithm for cutting a dendrogram

A dendrogram is a data structure used with hierarchical clustering algorithms that groups clusters at different "heights" of a tree - where the heights correspond to distance measures between clusters. After a dendrogram is created from some input…
Siler
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Affinity Propagation preferences initialization

I need to perform clustering without knowing in advance the number of clusters. The number of cluster may be from 1 to 5, since I may find cases where all the samples belong to the same instance, or to a limited number of group. I thought affinity…
alessandro.ferrari
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Machine Learning: Unsupervised Backpropagation

I'm having trouble with some of the concepts in machine learning through neural networks. One of them is backpropagation. In the weight updating equation, delta_w = a*(t - y)*g'(h)*x t is the "target output", which would be your class label, or…
Noob Saibot
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How to speed-up k-means from Scikit learn?

On my project I have used k-means to classify data between groups, but I have a problem with the computation of the k-means from Scikit-learn - it was very slow. I need to boost it. I have tried to change the number of n_jobs to -1, but still very…
user8058941
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Restricted Boltzmann Machine for real-valued data - gaussian linear units (glu) -

I want my Restricted Boltzmann Machine to learn a new representation of real-valued data (see: Hinton - 2010 - A Practical Guide to Training RBMs). I'm struggling with an implementation of Gaussian linear units. With Gaussian linear units in the…
theo
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What is the relation between topic modeling and document clustering?

Topic modeling identifies distribution of topics in a document collection, which effectively identifies the clusters in the collection. So is it right to say that topic modeling is a technique to do document clustering?
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