Questions tagged [machine-learning]
Questions about computer algorithms that automatically discover patterns in data and make good decisions based on them.
1,009
questions
0
votes
0answers
27 views
Is it possible to train a neural network to solve NP-complete problems?
I'm sorry if the question is not relevant, i have tried to search for articles about it but i couldn't find satisfying answers.
I'm starting to learn about machine learning, neural networks etc ... ...
-1
votes
0answers
10 views
I am looking for a way to assign numerical values to colors (then interpolate a color scale) and then assign numerical values to comparative colors
Hello may name is Chris
I am completely new to programming (but I love it!! I am learning Swift).
I am currently looking for a solution to the mentioned problem. I'm not sure if it is a ML task or if ...
1
vote
1answer
25 views
A pretrained model for mathematical equations characters detection
I am working on a project to convert equations to LaTeX code. After segmenting out the characters, I got stuck on the detection part and was looking for some pre-trained model that could detect ...
2
votes
0answers
16 views
How to understand mapping function of kernel?
For a kernel function, we have two conditions one is that it should be symmetric which is easy to understand intuitively because dot products are symmetric as well and our kernel should also follow ...
4
votes
1answer
22 views
Are non-ordered (Slabbed) Neural Networks in wide modern use and what does Fiesler (1994) mean by Clamping Function and Ontogenic function?
In Fiesler (1994) Neural Network Classification and Formalization, he talks a lot about a more general version of neural networks, one that is not ordered into layers, but rather the network is called ...
1
vote
1answer
45 views
How to do supervised learning without knowing the target
I'm working on an optimization algorithm that I think could be considered machine learning, but I'm not sure. Basically I have a model that I want to optimize by adjusting its parameters. I don't ...
1
vote
1answer
41 views
Books for the Mathematical Theory of AI/ML
I am interested in the mathematical foundations of Artificial Intelligence and Machine Learning. Are there any books which will describe and present the mathematical foundations in detail? I am not ...
0
votes
0answers
15 views
Event Detection
Can someone explain what is Event Detection? What activities or events fall under the purview of event detection? I have searched for some definitions but I am unable to understand. Are events like ...
0
votes
0answers
31 views
Delta rule for binary step function
I have a question of understanding about the Delta Rule:
$\Delta w_i = (y - \hat{y}) \times x_i$
Why does $x$ have to be multiplied again after the difference? If the input is $0$, the product of $w$...
1
vote
1answer
43 views
Method for combining derivative free optimization results of different data inputs
I am working on an algorithm that has multiple fixed parameters. The algorithm analyzes time series data and spits out a number. The fixed parameters need to be such that this number is as small as ...
2
votes
1answer
37 views
O(m+n) Algorithm for Linear Interpolation
Problem
Given data consisting of $n$ coordinates $\left((x_1, y_1), (x_2, y_2), \ldots, (x_n, y_n)\right)$ sorted by their $x$-values, and $m$ sorted query points $(q_1, q_2, \ldots, q_m)$, find the ...
0
votes
0answers
25 views
Problems about Decision Tree
Do any one give me some hints to solve this problem. Explain is steps by steps.Thanks
1
vote
2answers
27 views
Retrieving data from website - Parser vs AI
I'm currently working on a personal software project (as a hobby) and would like to know which kind of approach I should follow according to you.
My application scans some websites (like Amazon or ...
1
vote
1answer
34 views
PAC-learning definition
I am new in this field. In the book Foundations of Machine Learning by Mehryar Mohri, Afshi Rostamizadeh, and Ameet Talwalkar. The authors define the generalized error of a hypothesis as
$ R(h) = \...
0
votes
0answers
9 views
How Joint Probability Distributions are used to solve the problem of missing inputs in Classification
With n input variables, we can now obtain all 2^n diļ¬erent classiļ¬cation functions needed for each possible set of missing inputs, but the computer program needs to learn only a single function ...
0
votes
0answers
23 views
Is class of threshold functions Agnostic PAC Learnable?
In "Understanding Machine Learning, From Theory to Algorithms" by Shalev and Ben-David, on page 44 example 6.1, it is proved that the class of threshold functions are PAC learnable.
on the other hand, ...
0
votes
1answer
53 views
Can you share some pointers to get better at working alone in projects?
I am an ML Engineer who works alone in projects most of the time. I don't have people who are much smarter than me working alongside.
Most of the time, I feel that working alone is a tedious job,one ...
0
votes
1answer
34 views
Face recognition system with a sparse dataset
I have a dataset of 100 ID's, each ID with only a single unique face image. How can I develop an effective face recognition system to recognise only these faces?
I was thinking Deep Learning but the ...
0
votes
0answers
21 views
Improving link prediction model so that it predict well facts with anti-symmetric relation types
Refering to this paper , I was trying to look at the task of link prediciton on the WNR11 dataset. I looked at the TuckER model and found that the TuckER model can do very well on the facts involving ...
0
votes
0answers
19 views
ML - What are the benefits of transforming variables with long tail?
What are the obvious benefits of transforming variables with long tail distributions? To extend it more, why the machine learning algorithms will perform better afer such a transformation?
1
vote
1answer
14 views
Math behind Multi-class linear discriminate analysis (LDA)
I have a question about Linear Discriminant Analysis (LDA) for the purpose of Dimensionality Reduction.
So I understand for the algorithm to calculate for $k$ projection vector(s) you need to ...
0
votes
0answers
42 views
Modified sign function VC dimenson
If we have $f:\mathbb{R} \rightarrow \{\pm 1\}$, and $\mathcal{F}$ and $\mathcal{F}'$, what are the VC dimensions of
$\mathcal{F} = \{sign(\prod_{i=1}^n (x-\theta_i), \forall a_i \in \mathbb{R} \}$
$\...
0
votes
0answers
14 views
How to choose better performance based on margins from two kernels
In SVM, how can or cannot the margin attained by two different kernels on a single dataset be used to determine which classifier has better performance on the dataset?
Can we just plot the decision ...
0
votes
1answer
79 views
Classes of circle rims
The input space is a unit circle, $\mathcal{X} = \mathbb{S}^1 \subset \mathbb{R}^2$. There is class $\mathcal{F}$ of arcs on $\mathbb{S}^1$, where a point is labeled 1 if it is on the arc, and 0 ...
1
vote
0answers
33 views
How can a classifier using Laplacian kernel achieve no error on the input samples?
If we have a sample dataset $S = \{(x_1, y_i),\dots,(x_n,y_n)\}$ where $y_i = \{0,1\}$, how can we tune $\sigma$ such that there is no error on $S$ from a classifier using the Laplacian kernel?
...
1
vote
1answer
55 views
VC dimension of only the rim of a unit disk
Suppose we have an origin centered circle, ie $x^2 + y^2 =1$, so it's in $\mathbb{R}^2$ (2D). It will be classified as 1 if it lies only on this arc, and will be labeled 0 otherwise. What is the VC ...
0
votes
0answers
26 views
How to find clusters of a set of points in n-dimensional space that are furthest from unwanted points
I have a list of 25 points and their coordinates in a 512-dimensional space. I have 8 target points and 17 points I need to avoid (the 17 points to avoid also have differences in priority of how ...
2
votes
1answer
42 views
Labeled points in $\{0,1\}^n$ such that every linear separator requires exponential weights
I want to find labeled samples in $\{0,1\}^n$ such that the Perceptron algorithm takes $2^{\Omega(n)}$ steps to converge. One way to do this would be to find a sequence of labeled examples that are ...
0
votes
1answer
22 views
How do you calculate the training error and validation error of a linear regression model?
I have a linear regression model that I've implemented using Gradient Descent and my cost function is a Sum of Squares Error function. I've split my full dataset into three datasets, a training set, a ...
0
votes
0answers
18 views
Stochastic Gradient Descent for Multi-Class SVM
I'm trying to compute the optimization problem for a multi-class SVM loss function with $L2$ regularization.
$\displaystyle f(W) = \frac{1}{n}\sum_{i=1}^n\sum_{c\neq y_i} \max\{0,1-w_{y_i}^Tx_i+w_c^...
1
vote
0answers
10 views
Algorithm Sequence classification(anomaly detection)
I have a problem statement where i am having many inputs lets say A,B,C,D.... and this inputs can take any real value(e.g A-a1,a2,a3, B-b1,b2). Now, i have file which will be have record of the all ...
1
vote
0answers
11 views
How can one algorithmically define the required amount of centroids in K-Means clustering?
Say I have a dataset of n vectors. These are, by nature, clustered so that there is a significant distance difference between any two points within a cluster and any two points in separate clusters.
...
1
vote
2answers
78 views
How can Machine Learning be used to find attributes/characteristics of graphs?
I am aware ML is not necessary for many graph classification problems (as the graph theorists have many clever solutions), but I'm specifically interested in ML approaches to these types of questions. ...
0
votes
0answers
9 views
FOIL system for learning PROLOG rules from facts
I'm trying to use FOIL, a system from the 1990s developed by Ross Quinlan, to learn Prolog rules from facts. However, the input file syntax is tricky for me. Could someone provide me an example input ...
0
votes
1answer
21 views
Uniform Convergence and VC Theroy
I've started reading more about statistical learning theory, specifically this paper right here and I cannot understand the following part:
It turns out the conditions required to render empirical ...
1
vote
0answers
11 views
Why do we need to change the (weight decay) regularization parameter when changing the number of inputs that neural network is being trained with?
I am currently working my way through Michael Nielsen's ebook Neural Networks and Deep Learning and I am reading about overfitting and (L2) regularization. In this subsection, the process of L2 (a.k.a ...
0
votes
0answers
19 views
How to get synonyms from definitions using Bayes algorithm
I work on a project: an online dictionary where the user can find definitions of a word he's looking for.
My supervisor asked us to add synonyms using machine learning. He recommend us to use Bayes ...
2
votes
0answers
20 views
Simple back propagation example
Sorry if this is too simplistic of a question, but over the last couple of months I have been working through the course mathematical foundations of machine learning at my college. I think I am really ...
1
vote
0answers
22 views
Backprop formula question
I'm reading this chapter https://www.deeplearningbook.org/contents/mlp.html of the Deep Learning book, and on page 209, they have this equation (assume there is no regularizer and no bias parameter):
$...
1
vote
1answer
24 views
Proof of uniform convergence if VC dimension is finite
In the book Ā»Understanding Machine Learning: From Theory to AlgorithmsĀ«, written by Ben-David and Shalev-Shwartz, there is a proof which I do not understand. The context is proving that if a ...
1
vote
1answer
19 views
Learning a specific functional form with machine learning
Suppose I have only three independent features (x, y, z) as the input to some machine learning routine (e.g. neural network). From some domain knowledge, I know ...
0
votes
0answers
17 views
proof that SimpLe is fully expresive
Refering to this paper "SimplE Embedding for Link Prediction in Knowledge Graphs"
by Seyed Mehran Kazemi and David Poole in 2018 :
In page 4, about the proof of SimpLE being fully expressive,
I ...
1
vote
0answers
12 views
Equation to find the collaboration between neighbors in SOM in unsupervised learning
In Kohonen's SOM algorithm, the equation to find the collaboration is:
$$
\mathit{Damp}(i,j) = \exp\left(-\frac{\mathit{LDist}(i,j)^2}{2\sigma^2}\right)
$$
I know that LDist is the lattice distance ...
0
votes
0answers
28 views
training SimpLE model for link prediction on knowledge graph
Referring to this paper by Prof Kazemi, Prof Poole on SimpLE model for link prediction on knowledge graph.
In page 3, the paragraph on learning SimpLE Models, I understand that we have a batch of ...
1
vote
2answers
25 views
Time efficient way to implement Multi-Armed-Bandits?
I'm doing a research on Multi-Armed Bandit (MAB) problem with approx. 1 million arms. In contrast, the number of iterations is of course much larger, about 10-20 million.
Most MAB-algorithms require ...
0
votes
1answer
36 views
Is Artificial intelligence simply taking decisions on the basis of values produced by a machine learning model
I am researching on AI and its working. Whenever I try to search for AI algorithms, ML algorithms come up. Then, I read the differences between ML & AI. One of the key points mentioned was "AI is ...
0
votes
0answers
36 views
how many parameters do we need to estimate for a general probabilistic model
Its a question from a test in machine learning.
I have 3 binary variables x1,x2 and x3 (which means that each one of them can be either 1 or 0), each one of them has a binary output y (can be 0 or 1).
...
0
votes
0answers
22 views
Artificial Neuron: question on definition and inhibited signals
This is an artificial neuron of a NOT function from Rojas's ML book. I have a question on it's behavior. It's my understanding that the neuron produces a signal if it's inputs $x_1$ summed are $\geq ...
0
votes
1answer
51 views
Approximating Deep Neural Networks (DNNs) with Binarized Neural Networks (BNNs)
I am working currently as a research intern on Binarized Neural Networks where the weights and the activations of the network are binary. The architecture of this type of networks makes them memory ...
1
vote
0answers
44 views
Calculate probability in graphical model
I have the following graphical model, in which I wish to compute $p(Intelligence = 1|Letter = 1, SAT = 1)$
But I'm not sure how to rewrite $p(Intelligence = 1|Letter = 1, SAT = 1)$? I was told to ...
