Questions tagged [supervised-learning]

For questions related to supervised learning.

Filter by
Sorted by
Tagged with
1 vote
2 answers
73 views

"a good model (with low loss) is one that assigns a high probability to the true output $y$ for each corresponding input $\mathbf{x}$"?

Chapter 1.2.1.6 Maximum likelihood estimation of Probabilistic Machine Learning: An Introduction by Kevin P. Murphy says the following: When fitting probabilistic models, it is common to use the ...
The Pointer's user avatar
1 vote
1 answer
26 views

Since $f_c$ returns the probability of class label $c$, we require $0 \le f_c \le 1$ for each $c$, and $\sum_{c = 1}^C f_c = 1$. Why avoid this?

Chapter 1.2.1.5 Uncertainty of Probabilistic Machine Learning: An Introduction by Kevin P. Murphy says the following: We can capture our uncertainty using the following conditional probability ...
The Pointer's user avatar
0 votes
1 answer
35 views

Circular regression for joystick movements?

I've been playing around with some behavioral cloning of a simple old game that uses a joystick. As with behavioral cloning in general, if I record many games, then for each state there are many ...
eof's user avatar
  • 121
0 votes
1 answer
51 views

How can I combine unsupervised learning with supervised learning?

I am currently using an isolation forest (from sklearn library) to detect anomalies in a data frame (basically it's a dynamic data frame more of a kind of time series I am. But I have certain criteria ...
SUNITA GUPTA's user avatar
0 votes
0 answers
14 views

How to extract features from patterns in time series data

I have a time series welding data I wanted to create a model which can predict some weld parameters but extracting those parameters from time series data is being so difficult. Currently I tried ...
THUNDER 07's user avatar
0 votes
0 answers
12 views

Does the order of iteration affect the answer returned by FIND-S?

This paragraph is from the book Machine Learning by Tom M.Mitchell (Page 26): Initialize $h$ to the most specific hypothesis in $H$ For each positive training instance $x$ $\;\;\;\;\;\;$.For each ...
Emad's user avatar
  • 215
0 votes
1 answer
22 views

Can we generate labels for an unlabelled dataset by doing some feature engineering?

I am very new to ML and currently, I am working on building a model that can predict recurring blood donors (a classification problem). I have a dataset which consists of 25 features (gender, height, ...
stkmnd's user avatar
  • 1
0 votes
0 answers
5 views

Mechanism of Prediction Readjustment in Supervised Learning and Role of Self-Attention in Sequence Data Relationships

In supervised learning, when the prediction deviates significantly from the expectation, how does it "readjust"? And... LLMs are a subset of deep learning, just as generative AIs are. Is the ...
Assandra Lakal's user avatar
0 votes
0 answers
31 views

Any research in "probe-tuning" of LLMs?

Is there any research in "probe-tuning" of LLMs, i.e., tuning LLM's parameter weights such that a specific probe (classifier) is more reliably detecting certain markers throughout the ...
leventov's user avatar
  • 101
0 votes
1 answer
35 views

How can CAPTCHAs be used for both user verification and ML training?

CAPTCHAs (e.g. requiring a site visitor to click all the images of traffic lights in a grid of images) are often used throughout to Internet to verify that a site visitor is a human rather than a bot. ...
tparker's user avatar
  • 101
0 votes
1 answer
51 views

Is validation data needed with generated training data?

I have two systems, a system A that generates some data X and a system B that calculates ...
n-l-i's user avatar
  • 113
0 votes
0 answers
36 views

Is regression method the best for my case?

newbie here. I'm starting to work on a custom model for a very specific task, so I found no pre-trained models for this task so far. After checking (un)supervised learning approaches I believe that ...
Putnik's user avatar
  • 101
1 vote
1 answer
42 views

How to introduce variation between instances of a neural network?

What are some good ways to introduce variation between instances of a neural network? I've heard about training each instance on different data, with the same data in a different order, through ...
n-l-i's user avatar
  • 113
0 votes
0 answers
21 views

How can I transform a signal into another using supervised learning?

I'm trying to transform a signal into another using supervised learning. My main goal is to create a model capable to transform a raw signal (Blue Line) into something similar to the "ideal" ...
Nathaldien's user avatar
2 votes
1 answer
173 views

Reinforcement learning with $\gamma=0$ versus supervised framework [duplicate]

I am on a learning phase (still to enter the details but I would like first to get a bird's eye overview). I would like to understand the difference between: (1) a reinforcement learning framework, ...
Thomas's user avatar
  • 153
1 vote
0 answers
31 views

Is there some neural network that implements Least Squares?

I would like to build supervised NN that gets a matrix $A$ and vector $b$ as inputs and returns $x$ as a close result of the Least Squares algorithm for $Ax=b$. I looked for so works in the field and ...
ChaosPredictor's user avatar
0 votes
0 answers
7 views

Surveys, Papers, Hand on Tutorials about training data generation for anomaly detection

I am searching for anything related to supervised, semi supervised or unsupervised anomaly detection w.r.t training data generation. I am looking toward reading any work that tackles the issue how to ...
Skobo Do's user avatar
0 votes
1 answer
105 views

How does Supervised learning models handle time-varying data

I need to train a supervised learning model which would take some input which differs in its output relating to time. to better understand my question I would give a simple binary classification, the ...
L.Adham's user avatar
1 vote
1 answer
22 views

Identify features don't hold required information in a ML problem

given a ML model that performs poorly, can you differentiate between those two causes: bad architecture/not enough data the features do not hold enough information to solve the problem I'm ...
Gilad Deutsch's user avatar
1 vote
0 answers
70 views

How can I learn to transform one input signal (time series) into another?

I'm posting this question here because I've been trying in vain to solve a problem for weeks and I hope some of you might have some useful suggestions. Basically, the problem is as follows. I have 7 ...
balchicc's user avatar
1 vote
1 answer
43 views

How to use information on a function to design a neural network learning that function?

I have a function $g$ that takes a vector $x$ of size $n$ and an integer $k$ in $1, \ldots, n$. I know this function is of the form $$g(x,k) = G\left(\sum_{i=1}^k f(x_{i})\right),$$ where $f$ and $G$ ...
Aristodog's user avatar
  • 111
1 vote
2 answers
284 views

Machine Learning Models for Longitudinal Data

Recently, I had the following question about supervised classification models (e.g. random forest) for longitudinal data. Suppose I have the following data about students passing a fitness test - the ...
stats_noob's user avatar
0 votes
1 answer
47 views

Distinguishing text with opposite meanings in SVM (False Information Detection)

I am currently working on a Binary Text Classification Model (False Information Detection) using Support Vector Machine and used TF-IDF as text vectorizer in Python. I have already tried training the ...
alexand88r's user avatar
0 votes
1 answer
63 views

How can I calculate the parameter $w$ in the third condition of LVQ 2.1 algorithm?

I'm developing a neural network software using several NN architectures including LVQ family. I met a parameter that is used in the 3rd condition of LVQ2 and later versions. It's named $w$ and is used ...
uygur's user avatar
  • 1
0 votes
0 answers
25 views

What's the best way to train data with unbalanced targets?

Suppose I have data I want to use for supervised learning, but there is a pretty bad target/class/labels imbalance. Should I: Limit the size of the training set to make sure there is a flat target/...
sangstar's user avatar
  • 131
0 votes
1 answer
113 views

Is the noise term $\epsilon$ in $y=g(x) + \epsilon$ used to denote the model's imperfection to the real world?

In supervised machine learning, it is common to say that we learn a function of the form $$y=g(x) + \epsilon.$$ Generally, $\epsilon$ is used to denote noise or, more precisely, any influence by ...
Micha Christ's user avatar
2 votes
1 answer
205 views

What problem does the neural network really solve?

In the image below taken from a Youtube video, the author explains that the neural network can be used to fit a relational graph for a set of data points shown by the green line. And that this is ...
LinusMagnola's user avatar
0 votes
0 answers
59 views

What are some machine learning frameworks for supervised clustering?

I have a task where I need to take "data points" which consist of collections of items. Each item needs to be categorised according to predefined categories. That's the easy part - my ...
Alexander Soare's user avatar
0 votes
0 answers
31 views

Non-locally Electrically Programmable Logic Gates - Technological Advances Progress

Preface: I’d like to clarify that I understand what a relay is and that a PLC uses a fairly conventional microprocessor that only digitally establishes logical logic gate configuration as a digitally ...
Anony Mous's user avatar
1 vote
0 answers
488 views

(explore-exploit + supervised learning ) vs contextual bandits

Lets take an ad recommendation problem for 1 slot. Feedback is click/no click. I can solve this by contextual bandits. But I can also introduce exploration in supervised learning, I learn my model ...
dksahuji's user avatar
  • 111
0 votes
1 answer
54 views

Extracting keywords from messages

I'm starting a project where I want to extract keywords from given messages. The keywords are for example something like: "hard disk", "watch" or other technical components. I'm ...
gurke's user avatar
  • 1
1 vote
0 answers
105 views

Backpropagation not working as expected

I'm new to neural networks and I try to make a model that is guessing if a point is below or above relative to a function output. The idea is inspired from this video https://youtu.be/DGxIcDjPzac . ...
Valentin Stamate's user avatar
0 votes
0 answers
611 views

How can my CNN produce an "unknown" label?

I have a dataset of 20k images of infected mango. I have built a web-based app using Flask, where a user can upload a picture, and my CNN model detects the disease. I have 6 classes in the model, ...
Tahmeed's user avatar
2 votes
1 answer
74 views

How to handle class imbalance when the actual data are that way

My supervised learning training data are obtained from actual data; and in real cases, there's one class that happens less often than other classes, just around 5% of all cases. To be precise, the ...
Dan D.'s user avatar
  • 1,283
1 vote
0 answers
34 views

Is this a supervised or reinforcement learning problem, and which algorithm should I use to solve it?

I have a time series data with a little unusual cost/reward function (I haven't seen it before) The model must predict a $Y$ value for any $X(t)$. The reward is computed as follows. The model will ...
MohammadAli Zeraatkar's user avatar
1 vote
0 answers
24 views

Can you correlate decision boundary of final layer of a neural network to predictive distribution?

I was reading in a On the Decision Boundary of Deep Neural Networks that the final layer of a MLP can be equated to an SVM and can generate decision boundaries similar to methods with SVM. I was ...
user8714896's user avatar
3 votes
1 answer
615 views

Can we use ML to do anything else other than predicting (in the case of mathematical problems)?

(The math problem here just serves as an example, my question is on this type of problems in general). Given two Schur polynomials, $s_\mu$, $s_\nu$, we know that we can decompose their product into a ...
SmoothKen's user avatar
  • 153
1 vote
0 answers
22 views

Is there any known approach to generate sets of objects?

I am looking for some known approach, or some previous work, on the following problem: Let $\Sigma$ be an alphabet of symbols and $\Sigma^*$ be the set of all the strings that you can compose from ...
olinarr's user avatar
  • 755
1 vote
1 answer
97 views

Aside from specific training sets, what distinguishes the capabilities of different AI implementations?

(Disclaimer: I don't know much about ML/AI, besides some basic ideas behind it all.) It seems like ML/AI models can often be boiled down to statistics, where certain levers (weights) get fine-tuned ...
Fly's user avatar
  • 111
2 votes
0 answers
36 views

Literature on the advantages of using an auto-encoder for classification

Given a supervised problem with X, y input pairs, one can do two things for obtaining the function f that maps X with y with Neural Networks (and in general in machine learning): Deploy directly a ...
Tommaso Bendinelli's user avatar
0 votes
1 answer
300 views

What is the meaning of these equations in Noise2Noise paper?

I am trying to understand what is meant by following equations in the Noise2Noise paper by Nvidia. What is meant by the equation in this image? What is $\mathbb{E}_y\{y\}$? And how should I try to ...
Markov's user avatar
  • 41
0 votes
2 answers
84 views

Which models can I use for supervised learning with images?

I have to do a project that detects fabric surface errors and I will use machine learning methods to deal with it. I have a dataset that includes around six thousand fabric surface images with the ...
Ali Haydar Kurban's user avatar
0 votes
1 answer
92 views

Is training a CNN object detector on an image containing multiple targets that are not all annotated will teach it to miss targets?

I want to train a convolutional neural network for object detection (say YOLO) to detect faces. Consider this image: In this training image, I have many people, but only 2 of them are annotated. Is ...
S.E.K.'s user avatar
  • 71
4 votes
2 answers
1k views

What is the meaning of "exploration" in reinforcement and supervised learning?

While exploration is an integral part of reinforcement learning (RL), it does not pertain to supervised learning (SL) since the latter is already provided with the data set from the start. That said, ...
Tfovid's user avatar
  • 187
1 vote
0 answers
14 views

Multiple Inertia sensors system based for gestures recognition

I am a newbie to Machine Learning field as I am engaging to a personal project that I am trying to use the 6 degree of freedom Inertial Measurement Units(IMUs) measuring the Acceleration acting on 3 ...
Nguyen Trieu's user avatar
2 votes
1 answer
162 views

How can we teach a neural net to make arbitrary data associations?

Let's say I have pairs of keys and values of the form $(x_1, y_1), \dots, (x_N, y_N)$. Then I give a neural net a key and a value, $(x_i, y_i)$. For example, $x_i$ could be $4$ and $y_i$ could be $3$, ...
iamPres's user avatar
  • 116
3 votes
1 answer
2k views

How to generate labels for self-supervised training?

I've been reading a lot lately about self-supervised learning and I didn't understand very well how to generate the desired label for a given image. Let's say that I have an image classification task, ...
Vesko Vujovic's user avatar
0 votes
0 answers
44 views

Finding whether an input column is missing

I am working on a problem similar to this one:(supervised, artificial data) ...
munichmath's user avatar
1 vote
0 answers
42 views

What is a multi channel supervised classifier?

I came across a paper that describes its model architecture in the following way. Our TRIL network is a two-channel network jointly trained to predict the expert’s action given state and the system’s ...
calveeen's user avatar
  • 1,261
1 vote
0 answers
52 views

Advantages of training Neural Networks based on analytic success criteria

What is the reason to train a Neural Network to estimate a task's success (i.e. robotic grasp planning) using a simulator that is based on analytic grasp quality metrics? Isn't a perfectly trained NN ...
EmVee's user avatar
  • 11