# Questions tagged [machine-learning]

Machine learning, a branch of artificial intelligence, is the science of getting computer systems to meaningfully act without being explicitly programmed by human.

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### Apply PCA on very large sparse matrix

I am doing a text classification task with R, and I obtain a document-term matrix with size 22490 by 120,000 (only 4 million non-zero entries, less than 1% entries). Now I want to reduce the ...
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### Python OSS alternatives for Matlab Neural Network Toolbox. Any intercomparisons?

I'd like to be independent of commercial software for my scientific work. I find a dependence an commercial packages such as Matlab and its toolboxes unsatisfactory, because I do not know if I will ...
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### Why aren't Krylov subspace methods popular in the Machine Learning community compared to Gradient Descent?

Historically, iterative methods for solving relatively simple-structured systems $Ax=b$ with $A$ being a $4\times 4$ matrix or to find the eigenvalues of that matrix assuming in both problems that $A$ ...
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### Do RBF kernel matrices tend to be ill-conditioned?

I use RBF kernel function to implement one kernel based machine learning algorithm(KLPP), the resulting kernel matrix $K$ $$K(i,j)= \exp\left({\frac{-(x_{i}-x_{j})^2}{ \sigma_{m}^2}}\right)$$ is ...
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### Calculating Lagrange coefficients for SVM in Python

I'm trying to write a full SVM implementation in Python and I have a few issues computing the Lagrange coefficients. First let me rephrase what I understand from the algorithm to make sure I'm on the ...
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### Predict runtimes for dense linear algebra

I would like to predict runtimes for dense linear algebra operations on a specific architecture using a specific library. I would like to learn a model that approximates the function $F_{op} \;::\;$...
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### Markov (Chain) image generators?

Markov Chains can be used to generate, or auto-complete, text. https://en.wikipedia.org/wiki/Markov_chain#Markov_text_generators Training text is read, and some information about the text is ...
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### Use of machine learning in computational fluid dynamics

Background: I have only built one working numeric solution to 2d Navier-Stokes, for a course. It was a solution for lid-driven cavity flow. The course, however, discussed a number of schemas for ...
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### fmincg implementation in Python

I'm trying to re-implement Neural Networks in Python. I implemented the cost function and the backpropagation algorithm correctly. I have checked them by executing its Octave equivalent code. But ...
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### Learning parameters of noise and filter coefficients from data where data and noise both have Gaussian distributions

Assume $X$ and $N$ are two sets of vectors (observations) from two different normal distributions, where $X$ represents clean data and $N$ represents noise; and $A$ a projection matrix of a filter. ...
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### Testing for stability of a simulated dynamical system

Background and question I often work with simulations of dynamical systems and I usually track a single parameter $x$, such as the number of agents (for agents based models) or the error rate (for ...
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### Support Vector Machines as Neural Nets?

This is more of a conceptual question. I have learned about Neural Nets, and I have some clue as to how Support Vector Machines work. I read somewhere however that given the appropriate kernel (is ...
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### Books/Resources on Sparse Optimization?

I'm looking to learn more about Sparse Optimization and apply it to machine learning problems. Could you please recommend some books/resources on this topic? Both theoretical and applied are fine.
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### Are there tasks in machine learning which require double precision floating points?

Double-precision calculations are significantly slower or more expensive than single-precision calculations. For example, the NVidia Tesla which performs well on doubles is much more expensive then ...
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### Learning computational science through guided discovery

I am currently trying to get through Pattern Classification by Duda et al (for a course). However, the book seems too dense for me. Pattern recognition seems like a topic that could be better learned ...
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### Logistic regression with Python

I am trying to code up logistic regression in Python using the SciPy fmin_bfgs function, but am running into some issues. I wrote functions for the logistic (...
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### Using an approximation algorithm to adapt parameter values of a given algorithm

Problem: I have an incremental online clustering algorithm which need 4 parameters that should be specified by the user before execution. The algorithm will gives "good results" if "a good parameter ...
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### Deep learning using Distributed linear algebra

Is there any deep learning library based on Trillinos or Petsc linear algebra?
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### How to train a model to classify object trajectories?

I have a tracker that outputs the trajectory $(x,y,z)$ of an object (e.g., a can). I want to use these trajectories to train a classifier (i.e., SVM) in order to infer the activity that the person ...
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### Looking for an understandable discussion of creating Maximum Entropy classifiers

Texts, articles, and papers on Maximum Entropy Classifiers tend to come in two varieties: the more popular "upper level", and the more technical. The popular variety are good at explaining the ...
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### Are the No Free Lunch Theorems Useful for Anything?

I have been thinking about the No Free Lunch (NFL) theorems lately, and I have a question which probably every one who has ever thought of the NFL theorems has also had. I am asking this question here,...
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### Machine Learning for Optimization

I have a function which takes 100+ coefficients and outputs $x$. I wish to optimise $x$. Running the simulation 50 000 times will take around 15 minutes, however, this happens in parallel - and the ...
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### Interpolation vs. Neural network

I am seeking knowledge from the community. I am solving a transport PDE (conservation of solute mass) using COMSOL. At each Newton-Raphson iteration, I need to update a constant called $Kd$ for some ...
In order to achieve a 2D representation $X\in\mathbb{R}^{n\times 2}$ of some high-dimensional data residing in $Y\in\mathbb{R}^{n\times k}$, I use PCA:$$X=Y\cdot U,$$where $U\in\mathbb{R}^{k\times 2}$ ...