Data & Analytics Exam > Data & Analytics Videos > Weka Tutorial > Weka Tutorial 26: Semi-supervised Learning (Learning Techniques)
Weka Tutorial 26: Semi-supervised Learning (Learning Techniques) Video Lecture | Weka Tutorial - Data & Analytics
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FAQs on Weka Tutorial 26: Semi-supervised Learning (Learning Techniques) Video Lecture - Weka Tutorial - Data & Analytics
| 1. What is semi-supervised learning? |  |
Ans. Semi-supervised learning is a machine learning technique that combines both labeled and unlabeled data for training a model. It aims to leverage the large amount of unlabeled data available to improve the performance of the model in making predictions.
| 2. How does semi-supervised learning differ from supervised learning? | |
Ans. In supervised learning, the model is trained using only labeled data, where each data point is associated with a target label. On the other hand, semi-supervised learning uses both labeled and unlabeled data, with the assumption that the unlabeled data contains valuable information that can assist in learning.
| 3. What are the advantages of using semi-supervised learning? | |
Ans. Semi-supervised learning offers several advantages. It allows leveraging a large amount of unlabeled data, which is often easier and cheaper to obtain compared to labeled data. This can lead to improved model performance, especially when labeled data is scarce. Additionally, semi-supervised learning can help in handling class imbalance and noisy labeled data.
| 4. What are some popular algorithms used in semi-supervised learning? | |
Ans. There are various algorithms used in semi-supervised learning. Some popular ones include self-training, co-training, generative models such as Expectation-Maximization, and graph-based methods like label propagation. Each algorithm has its own strengths and assumptions, and the choice depends on the specific problem and data characteristics.
| 5. What are the challenges in semi-supervised learning? | |
Ans. Semi-supervised learning faces several challenges. One challenge is the quality of the unlabeled data, as it may contain noise or irrelevant information. Another challenge is selecting the appropriate amount of labeled and unlabeled data to achieve the best performance. Additionally, the assumptions made by the algorithms may not always hold true in real-world scenarios, leading to suboptimal results.
Text Transcript from Video
hello YouTube welcome to another wake a
tutorial this tutorial is very important
because in this tutorial I'm going to
tell you how you can use semi-supervised
learning algorithms with wake up as you
know already that wake are developed by
University of Waikato doesn't have the
implementation of semi-supervised
learning algorithms it only has the
implementation of supervised and
clustering algorithms now if you were a
machine learning person then you might
have known that
nowaday semi-supervised learning
algorithms are popular choice because on
many occasions you can have much less
number of labeled data so whenever your
data set is not labeled that measure
it's very expensive to label data sets
in those cases semi-supervised learning
can be a viable option so wicked doesn't
have it then how are we going to
implement this in wake up well we don't
have to do it because as well cat is an
open source software there are many
people currently working on developing
semi-supervised learning algorithms in
Java and then they integrate that with
the wake of releases so these are the
files that I found after the extraction
of those five rar or zip files so in
this file you can find one folder called
target double click on that and you can
see a jar file executable Java archive
file called collective - classification
all you need to do is double click on
that so this one is actually opening or
running or executing the waker 3.7
version but integrated with
semi-supervised learning algorithm so
this is what a 2.7 + semi-supervised
learning algorithms so how do they look
like if we go to the Explorer and I'm
just randomly opening
one of my files from my disk actually
did this doesn't matter I'm just giving
you some demo so I'm choosing one of the
our files that I've produced in my
research I can go to I can click on the
classified tab go to the classifier
options and then you can see that we
have a new folder called collective so
this folder actually contains all the
semi-supervised learning algorithms
implemented by that group in Java and it
has been integrated with Weka 3.7 so if
you double click on that to open so you
can see there are soft folders that
means that we are having semi-supervised
learning outlining algorithms that can
be categorized into these four groups
functions lazy maida and trees so if I
close these three things you can see
that there are functions lazy maida and
tree here as well but these are the
supervised or clustering algorithms
produced by the University Waker is our
University of Waikato version of wake up
so if you go to the collective section
if you choose for example function ll GC
is a kind of semi supervised learning
algorithms and semi-supervised learning
algorithms to be honest are not as
popular as supervisor classification
sorry clustering algorithms so if you
don't know what this algorithm does or
how it works or what kind of data it
handles you can always select this one
ll GC or any other semi-supervised
learning algorithms under the collective
folder then you can left-click on these
options and you see that we have the
more button and you can click on the
more button to see how what it what it
is capable of doing and if you want to
know more about some of the some of the
implementation details of the algorithms
then you can always go back
to the references provided by the
author's here for example in order to
know ll GC you can always go back to
this 2003 paper that actually talks
about ll GC so this is kind of fun you
can play it play with it and you can
find out the power of algorithms
categorized under semi-supervised
tutorial this tutorial is very important
because in this tutorial I'm going to
tell you how you can use semi-supervised
learning algorithms with wake up as you
know already that wake are developed by
University of Waikato doesn't have the
implementation of semi-supervised
learning algorithms it only has the
implementation of supervised and
clustering algorithms now if you were a
machine learning person then you might
have known that
nowaday semi-supervised learning
algorithms are popular choice because on
many occasions you can have much less
number of labeled data so whenever your
data set is not labeled that measure
it's very expensive to label data sets
in those cases semi-supervised learning
can be a viable option so wicked doesn't
have it then how are we going to
implement this in wake up well we don't
have to do it because as well cat is an
open source software there are many
people currently working on developing
semi-supervised learning algorithms in
Java and then they integrate that with
the wake of releases so these are the
files that I found after the extraction
of those five rar or zip files so in
this file you can find one folder called
target double click on that and you can
see a jar file executable Java archive
file called collective - classification
all you need to do is double click on
that so this one is actually opening or
running or executing the waker 3.7
version but integrated with
semi-supervised learning algorithm so
this is what a 2.7 + semi-supervised
learning algorithms so how do they look
like if we go to the Explorer and I'm
just randomly opening
one of my files from my disk actually
did this doesn't matter I'm just giving
you some demo so I'm choosing one of the
our files that I've produced in my
research I can go to I can click on the
classified tab go to the classifier
options and then you can see that we
have a new folder called collective so
this folder actually contains all the
semi-supervised learning algorithms
implemented by that group in Java and it
has been integrated with Weka 3.7 so if
you double click on that to open so you
can see there are soft folders that
means that we are having semi-supervised
learning outlining algorithms that can
be categorized into these four groups
functions lazy maida and trees so if I
close these three things you can see
that there are functions lazy maida and
tree here as well but these are the
supervised or clustering algorithms
produced by the University Waker is our
University of Waikato version of wake up
so if you go to the collective section
if you choose for example function ll GC
is a kind of semi supervised learning
algorithms and semi-supervised learning
algorithms to be honest are not as
popular as supervisor classification
sorry clustering algorithms so if you
don't know what this algorithm does or
how it works or what kind of data it
handles you can always select this one
ll GC or any other semi-supervised
learning algorithms under the collective
folder then you can left-click on these
options and you see that we have the
more button and you can click on the
more button to see how what it what it
is capable of doing and if you want to
know more about some of the some of the
implementation details of the algorithms
then you can always go back
to the references provided by the
author's here for example in order to
know ll GC you can always go back to
this 2003 paper that actually talks
about ll GC so this is kind of fun you
can play it play with it and you can
find out the power of algorithms
categorized under semi-supervised
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Dec 23, 2024 Last updated |
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