Questions tagged [unsupervised-learning]
For questions about AI that learns without being provided with a set of labels (expected answers) along with the set of input examples.
56
questions
2
votes
1answer
25 views
Is a multi-layer Kohonen network possible?
The Kohonen network is one fully connected layer, which clusters the input into classes by a given metric. However, the one layer does not allow to operate with complex relations, that's why deep ...
0
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0answers
8 views
Predicting Hot Categories In a Reference Manager
Reference managers like Zotero or Mendeley allow researchers to categorize papers into hierarchical categories called collections. The User navigates through a listing of these collections when filing ...
0
votes
0answers
21 views
Dense bottleneck layer in Autoencoder
I would like to use the bottleneck layer of U-Net (last layer of the encoder) to calculate the similarity between two images. For that I have to somehow flatten the last layer of the encoder. In my ...
0
votes
0answers
17 views
Prediction of values with an unsupervised model
Given a set of historical data points, I am trying to predict a continuous output of which I have no historical record of, therefore the problem is of an unsupervised nature.
I am wondering if there ...
2
votes
1answer
37 views
Reconstruction Errors in Auto Encoders after Training
Autoencoders are used for unsupervised anomaly detection by at first learning the features of the data set with mainly "normal" data points. Then new data can be considered anomalous, if the new data ...
3
votes
2answers
91 views
Is there a machine learning algorithm to find similar sales patterns?
I have a dataset as follows
(and the table extends to include an extra 146 columns for companies 4-149)
Is there an algorithm I could use effectively to find similar patterns in sales from the other ...
2
votes
1answer
40 views
Which unsupervised learning algorithm can be used for peaks detection?
So, I have a dataset which has around 1388 unique products and I have to do unsupervised learning on them in order to find anomalies (high/low peaks).
The data below just represents one product. The <...
3
votes
0answers
29 views
References and books for unsupervised learning
I am looking for good introductory and advanced books in AI, especially unsupervised learning. I have already read books like Probabilistic Graphical Models from D. Kholler and Pattern Recognition and ...
0
votes
0answers
32 views
Unsupervised LSTM
I have a big amount of light curves (image below) and I am trying to label the points as signal or background (the signal appears usually periodically, several times, for a given light curve). However,...
3
votes
1answer
44 views
Variational Autoencoder task for better feature extraction
I have a CNN with the regression task of a single scalar.
I was wondering if an additional task of reconstructing the image (used for learning visual concepts), seen in a DeepMind presentation with ...
1
vote
0answers
13 views
Is there a way to compare the similarities among different graphs and then cluster them using Unsupervised learning?
I have a dataset about (240000,23). For my task, I have to use an unsupervised learning method and apply it on every single column separately in order to detect anomalies that might exist. I have pre-...
4
votes
1answer
86 views
Unsupervised Learning for anomaly detection
I've started working on anomaly detection in Python.
My dataset is a time series one. The data is being collected by some sensors which record and collect data on semiconductor making machines.
My ...
2
votes
1answer
77 views
What is graph clustering?
There are several (family of) algorithms that can be used to cluster a set of $d$-dimensional points: for example, k-means, k-medoids, hierarchical clustering (agglomerative or divisive).
What is ...
2
votes
2answers
58 views
Which unsupervised anomaly detection algorithms are there?
I need to create model which will find suspicious entries or anomalies in a network, whose characteristics or features are the asset_id, ...
0
votes
2answers
78 views
Where can I find an implementation of the wake-sleep algorithm?
I'm looking to build from scratch an implementation of the wake-sleep algorithm also known as an unsupervised neural network. I plan on doing this in Python in order to better understand how it works. ...
0
votes
0answers
16 views
Visualizing Clusters in Self Organizing Map
I am a bit confused about how can i visualize clusters in a Self Organizing Map. The input data is a set of images, where each image is an english alphabet in some font. Now if i have to visualize the ...
0
votes
0answers
25 views
identifying pattern in datasets
i am new to machine learning. i'm trying to identify driving pattern through accelerometer and gyroscope sensor. i have been collecting the data of both the sensors and have been storing them in .csv ...
0
votes
0answers
14 views
How to remove unwanted signals from the sensor measurement?
I have 2 tabular datasets, one is clean and one is drifted.
They are records of sensor measurements. I move the sensor around in the room and collected thousands of measurements.
I have a sensor that ...
3
votes
3answers
285 views
How to implement a Continuous Control of a quadruped robot with Deep Reinforcement Learning in Pybullet and OpenAI Gym?
Description
I have designed this robot in URDF format and its environment in pybullet. Each leg has a minimum and maximum value of movement.
What reinforcement algorithm will be best to create a ...
1
vote
0answers
40 views
AUTOENCODERS FOR CREDIT CARD FRUD DETECTION
Am working on credit card fraud detection problem using autoencoders. Regarding that I have some doubts given below :
The dataset for the above problem has been downloaded from kaggle which is highly ...
1
vote
0answers
27 views
a question about Zeiler's paper “Deconvolutional Networks”
In "4.1 Learning multi-layer deconvolutional filters" section, the last paragraph says that "Since our model is generative, we can sample from it. In Fig. 3 we show ...
1
vote
1answer
28 views
Steps for final Logistic Regression Modal
I am new for machine learning and I am tried to understand basic steps to get final modal of Logistic Regression.
I know Logistic Regression is supervisory learning technique. Therefore we want to ...
0
votes
1answer
39 views
Using unsupervised learning for classification problems
Let's say there are two types of cancer(Type 1 and Type 2). Say we want to see if one of pour friends has cancer Type 1 or 2. We can treat this as a classification problem. But what if we use ...
5
votes
2answers
357 views
Do GAN's come under Supervised Learning or Unsupervised Learning?
My guess is that they come under supervised learning, as we have labelled dataset of images, but I am not sure as there maybe other aspects in GANs which might come into play in the determination of ...
0
votes
1answer
33 views
What is the approach to deduce formal rules based on data?
We have data in text format as sentences.
The goal is to detect rules which exist in this set of sentences.
I have a limited set of contextless sentences that fit a pattern and want to find the ...
5
votes
1answer
290 views
Learning algorithms of Neural Networks
Could you please let me know which of the following classification of Neural Network's learning algorithm is correct?
The first one classifies it into:
supervised,
unsupervised and
reinforcement ...
4
votes
2answers
59 views
What techniques to explore for dynamic clustering of documents (emails)?
I have a dataset of unlabelled emails that fall into distinct categories (around a dozen). I want to be able to classify them along with new ones to come in the future in a dynamic matter. I know that ...
1
vote
1answer
126 views
Kohonen clustering of flowers
I have a question about output of my SOM network.
I have trained my network with diffrent size, learning rate and epochs, but my output always can recognise two big clusters. Iris-setosa and Iris-...
1
vote
2answers
71 views
Classifying non-labeled data with high dimensionality
Disclaimer: I am a novice in the world of machine learning, so please excuse my ignorance.
My dataset consists of things like age, days since last visit, etc. This information is medical related. ...
3
votes
2answers
239 views
Can agent based machine learning achieved with any ML algorithms other than neural network?
I would like to know other tha neural network, is there any ML technique for agent based ML. If so how to train an agent with some predefined rules? Can we use python programming for representing ...
6
votes
3answers
252 views
Has anybody tried unsupervised deep learning from youtube videos?
YouTube has a huge amount of videos, many of which also containing various spoken languages. This would presumably provide something like the data that a "challenged" baby would experience - "...
1
vote
0answers
51 views
Learning from events. Supervised, Unsupervised or MDP?
I have a large set of simulation logs for a market simulation of which I want to learn from. The market includes:
customers
products (subscriptions)
The customers choose products and then stick with ...
3
votes
2answers
69 views
Learning in unsupervised learning
I am not clear with the concept that an unsupervised model learns. We are giving an input and output to the supervised model so that it can generate a particular value, pattern or something out of it ...
4
votes
3answers
483 views
Is it possible to write an adaptive parser?
I am working on a js library which focuses on error handling. A part of the lib is a stack parser which I'd like to work in most of the environments.
The hard part that there is no standard way to ...
2
votes
1answer
60 views
Detect observations under certain conditions
I have a customer purchasing dataset and the data set is from a retailer having an online store and offline stores. So, customers have two options in their shopping channel, online or offline. In an ...
3
votes
2answers
137 views
Predict frequently purchased items under certain conditions with customer purchasing history data
I have purchasing history data for grocery shopping. I am trying to get abnormally frequently purchased items under certain conditions. For instance, I am trying to find frequently purchased items, ...
2
votes
1answer
137 views
Keras pattern finding between hash and word
My goal is to build a neural net that can find patterns between a hash and a word on it's own. So that it returns the word of any hash that I will input.
Unfortunatally my skill in the area of ...
4
votes
1answer
107 views
K-Armed Bandit and Reinforcement Learning
In the book "Reinforcement learning" by Sutton there is a discussion of the k-armed bandit problem, where the expected reward from the bandits changes slightly over time (is non-stationary). Instead ...
4
votes
2answers
442 views
At what rate could AI theoretically self-improve?
Due to recursive self-improvement, AI could lead to an intelligence explosion improving on itself year over year exponentially.
Assuming the proper environment was created to allow an AI to self-...
3
votes
1answer
307 views
Which Reinforcement Learning algorithms are efficient for episodic problems?
I have some episodic datasets extracted from a turn-based RTS game in which the current actions leading to the next state doesn’t determine the final solution/outcome of the episode.
The learning is ...
1
vote
0answers
26 views
Does it make sense to train an autoencoder using data from different distributions?
Say I have 500 variables and I believe those variables can be shown in a 4-dimensional latent representation which I want to learn.
What I have for training is 100K samples, and those samples are ...
1
vote
1answer
65 views
How does this part of algorithm works?(K-means)
I can't understand the red box. what do does that 1 do before {c(I)=j} ?
Also how does all the algorithms work? May someone ...
6
votes
1answer
770 views
Is the new Alpha Go implementation using Generative Adversarial Networks?
I read through the publication Mastering the game of Go without Human Knowledge. It doesn't seem to use GANs, just a new form of search and reinforcement learning.
2
votes
1answer
186 views
Does eligibility traces and epsilon-greedy do the same task in different ways?
I understand that in Reinforcement Learning algorithms such as q-learning, to prevent selecting the actions with greatest q-values too fast and allow for exploration, we use eligibility traces.
Here ...
5
votes
1answer
457 views
What makes learned feature detectors specialize in CNN?
It has been shown that it is possible to use unsupervised learning techniques to produce good feature detectors in CNNs. I can't understand what drives specialization of those feature detectors. In ...
10
votes
5answers
12k views
Using Machine/Deep learning for guessing Pseudo Random generator
Is it possible to feed a neural network, the output from a random number generator and expect it learn the hashing/generator function. So that it can predict what will be the next generated number? ...
6
votes
2answers
2k views
What is the difference between assisted and unassisted learning in relation to AI?
Is this related to supervised and unsupervised machine learning? Is it related to AI assisted human learning, and what is the distinction?
Also, why is assisted machine learning seen as an ...
5
votes
3answers
204 views
Why do Decision Tree Learning Algorithm preferably outputs the smallest Decision Tree?
I have been following the ML course by Tom Mitchel.
The inherent assumption while using Decision Tree Learning Algo is: The algo. preferably chooses a Decision Tree which is the smallest.
Why is ...
4
votes
1answer
230 views
Unsupervised alien natural language learning
While conducting research, I recently stumbled upon the deep learning and natural language processing concepts. In this question they say that the ‘grammar induction’ is a ‘supervised learning’ mode. ...
1
vote
1answer
341 views
Does neuroevolution require a labelled dataset?
A neuroevolution algorithm, such as DXNN, can be used to refine the topology and weights of an artificial neural network (ANN). The GA will require a fitness function, which means you need labeled ...
