Questions tagged [machine-learning]

Questions about computer algorithms that automatically discover patterns in data and make good decisions based on them.

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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 ... ...
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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 ...
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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 ...
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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 ...
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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 ...
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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 ...
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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 ...
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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 ...
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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 $w$...
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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 ...
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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 ...
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Problems about Decision Tree

Do any one give me some hints to solve this problem. Explain is steps by steps.Thanks
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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 ...
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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) = \...
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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 different classification functions needed for each possible set of missing inputs, but the computer program needs to learn only a single function ...
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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, ...
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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 ...
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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 ...
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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 ...
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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?
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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 ...
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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 $\mathcal{F} = \{sign(\prod_{i=1}^n (x-\theta_i), \forall a_i \in \mathbb{R} \}$ $\...
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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 ...
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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 ...
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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? ...
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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 ...
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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 ...
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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 ...
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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 ...
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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^...
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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 ...
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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. ...
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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. ...
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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 ...
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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 ...
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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 ...
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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 ...
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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 ...
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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): $...
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24 views

Proof of uniform convergence if VC dimension is finite

In the book »Understanding Machine Learning: From Theory to Algorithms«, written by Ben-David and Shalev-Shwartz, there is a proof which I do not understand. The context is proving that if a ...
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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 ...
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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 ...
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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 ...
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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 ...
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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 ...
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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 ...
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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). ...
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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 ...
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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 ...
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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 ...

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