Questions tagged [algorithms]

An unambiguous list of computational steps involved in finding a solution to a class of problems.

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Why a fully factorised approximation in message passing algorithms?

I would like to understand how to do Bayesian inference in a Bayesian network that contains a mixture of discrete and continuous random variables. There are two algorithms that seem like they do ...
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Formal approach to message passing algorithms

I'm trying to understand message passing algorithms, especially for the specific application of performing conditioning in a Bayesian network. My question is wether there is a mathematically precise ...
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How to fix the tree structure for a tree-based algorithm?

Background Some of our BI analysts and most of our managers are interested in making explainable predictions. One of our colleagues proposed an approach based on individual tree leaves from a tree-...
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Combinations from different sets with weightings

Imagine the following scenario: I want to create 1000 unique combinations of clothing. The combinations would include the following categories: hats, shirts, shorts, socks and shoes. Each combination ...
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Robust distance weighted mean

Given a data sample $\{x_i\}_1^n$, instead of hard omitting outliers by e.g. trimming, one can form a weighted average where we soft penalize observations out in the tails. \begin{align} \mu = \frac{...
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no free lunch theorem version 2

Let data-generating function $f$ be fixed, $D = \{(x_1,y_1),...,(x_N,y_N)\}$, $A$ is set of all possible deterministic learning algorithms and $h:X\rightarrow Y$ is a classifier trained by the ...
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how to implement a next position predictor machine learning model? [closed]

I am trying to implement next position predictor base on machine Learning algorithms.I want to know how can i start building my model ?
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3 answers
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No free lunch theorem proof

Assume that learning algorithm $A$ is fixed. Let $D = \{(x_1,y_1),...,(x_N,y_N)\}$, $F$ is set of a data-generating functions(meaning $f \in F$ then $f(x_i) = y_i$ and that functions in $F$ are ...
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DoE for optimization / control approach?

I'm wondering whether a DoE approach could somehow be used as kind of an optimization algorithm? One of my current tasks is to find a set of five parameter which max a sixth one (see here for more: ...
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clustering sets of numbers

I have some experimental measurements which come in sets $S_i$. Each $S_i$ contains some floating point numbers $s_{ij}$. I think the $S_i$ form clusters with similar contents. How do I assign the $...
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20 votes
3 answers
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Algorithm for sampling fixed number of samples from a finite population

I'm looking for an algorithm that would do the following: Imagine that you need to sample uniformly at random and without replacement $k$ elements from a pool of $n$ elements. The catch is that $n$ is ...
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For what do we need the expected value $\hat{\mathop{\mathbb{E}}}_t$ operation in PPO?

Given the PPO algorithm from reinforcement learning. $L^{CLIP}(\theta)=\hat{\mathop{\mathbb{E}}}_t \left[\min(r_t(\theta)\hat{A_t}, \text{clip}(r_t(\theta), 1-\epsilon, 1+\epsilon)\hat{A}_t)\right]$ I ...
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Evaluating randomized algorithms on randomized problem instances

Suppose we want to compare the performances of two algorithms — call them A and B — on a problem X. In particular, suppose that we want to evaluate the algorithms on random instances of X drawn from ...
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Why PC algorithm is order dependent?

I am studying constraint-based causal discovery algorithms like PC. From a paper, I learned that PC algorithm is order dependent. The paper said: "When the PC algorithm is applied to data, it is ...
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Do I need to use nested cross validation to select the best algorithm?

Suppose I want to compare the performance of several classification algorithms on a data set to choose the best algorithm to use with that data set. To do this, I run logistic regression, k-nearest ...
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Least likely sample in Multivariate Hypergeometric distributions

Let $U=(U_1,U_2,\dots,U_c)$ being an urn with $U_i$ balls of color $i \in [1,c]$ and $\Omega(U)$ be the set of possible draws from urn $U$. For $D \in \Omega(U)$ the probability of drawing $D$ is ...
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Why does my regression neural network completely fail to predict some points?

I would like to train a NN in order to approximate an unknown function $y = f(x_1,x_2)$. I have a lot of measurements $y = [y_1,\dots,y_K]$ (with K that could be in the range of 10-100 thousands) ...
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references of concept of scipy.signal.argelextrema

I am looking into the concept of algorithm code scipy.signal.argelextrema I understand the concept, but, I am trying to find an article\review\book chapter which ...
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Capital Y vs small y

I want to use the acceptance-rejection method to generate a sample from the following target probability density function: $$f\left(x\right)=1.25x^4+2x^3+0.25,\:0<x<1$$ Let the trial probability ...
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Why do gradient boosting algorithms mostly use trees?

Why do gradient boosting algorithms mostly use trees? Is there any logic in this? (in XGBoost and in boosting which in sklearn library uses trees, not other algorithms).
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Identify Stock Outages Using Only Sales Data

I have a dataset of sales data for a retailer for a number of SKUs. I'm trying to use this data to identify when a particular SKU was out of stock. I do not have purchase order data (the retailers ...
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Statistical/Automated method for identifying a dataset suitable for Machine Learning Modelling

Given a folder which has 10k Excel files, the objective is to identify the datasets suitable for Machine Learning Modelling Approach. We use a script right now which performs this operation and call ...
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What are the best ranking models availble?

I am looking to compare data for example with sports teams, compare recent and historical results. I want to be able to compare any two teams when needed and also take into account a players database ...
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Comparing Two Ensemble Methods

Algorithm1 uses a single base classifier as a member of the ensemble. Suppose the size is 5 and each member in the ensemble is a Naive Bayes. The training data is shuffled/sampled (may generate a ...
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4 votes
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Examples in the Real World where Evolutionary Algorithms/Genetic Algorithms Outperform other Classes of Optimization Algorithms

I have been trying to do some research to find out if there are certain industries/types of problems or even specific examples in applied research paper where Evolutionary Algorithms (e.g. Genetic ...
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Estimating a statistic by combining two different data sources

Say you want to estimate a statistic $\theta$ and have two data sources. A sample from data source A can be treated as a low-variance, somewhat biased estimate of $\theta$. A sample from data source B ...
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What are the best algorithms to predict a continuous target where I only have binary attributes? [closed]

Hello, I am new to machine learning and have a project where the dataset consists of binary attributes and the target("Pawpularity") has a continuous value. I was wondering if you could ...
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How can I reduce the number of times people are randomly assigned to the same team when using a random number generator?

I am randomly assigning people to play on different golf teams each week. I have 7 teams, each with 4 players. My starting list is ordered alphabetically by last name. I’m using a random number ...
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Vapnik's supposedly bounded functions

In his Statistical Learning Theory (1998), Vapnik presents the following mixture of two normal laws (p.236), in which the parameters $a$ and $\sigma$ are unknown: $$p(z;a,\sigma)=\frac{1}{2}N(a,\sigma)...
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What types of algorithms can deal with data spikes by distributing them into the prior values, rather than removing them?

I'm dealing with a dataset that has three types of inputs: Normal daily measurements Periodic 'data dumps' that contain additional values for the past 30 days Periodic 'data corrections' that might ...
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How would one measure consistent high values for a dataset? Is it the same as measuring a high variance for a low percentile?

For a numerical dataset, you can measure the consistency of the data using variance, where lower variance means the data is closer to the mean/median, while higher variance means the data is spread ...
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Algorithm design in data science [closed]

I am trying to enter into a data science job. I gave some internship interviews but have not had luck yet. Last week, I gave an interview where one of the panellists asked one hypothetical question: “...
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Data association on data from multiple cameras

Suppose we have several cameras that cover a certain area. In each camera we track a person. Each person have a path in global coordinates, timestamps and a feature-vector. The goal is to group these ...
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2 votes
1 answer
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Without looking at all the data, how can I test whether a collection only contains unique objects?

I have collection of data objects distributed across multiple machines. An O(n) lookup is not feasible, so I will need to sample. Is there an algorithm that I can use (preferably one that can relate ...
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1 answer
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Algorithm/method for grouping items based on their relative distance

I'm looking for a method to classify a set of items based on their relative distance. For example assume we have 4 cities and we know their relative distance: city1 city2 city3 city4 0 2.1 2.2 3.4 ...
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Different way to do PCA: overall comparison

Given a dataset PCA can be performed via 3 ways: Eigenvalue decomposition Singular value decomposition Non-linear iterative partial least-squares algorithm Can anyone shed light on comparative study ...
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RL algorithms for continuing task problems

I am stuck with an RL problem where the state space is continuous and the action space is discrete. The problem is a continuing task problem; there are no episode boundaries. So far, I have tried to ...
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Are there any General Proofs on Genetic Algorithms?

Are there any general proofs or theorems relating to "genetic algorithms"? I have been reading about a theorem in math called the "Schema Theorem" - this theorem is one of the ...
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Rule-Based and Tree-Based Statistical Models

I have been doing some research and have been trying to find "Rule-Based" and "Tree-Based" (statistical) models that are capable of overcoming the "greedy search algorithm&...
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Is there a concept for "almost parallel" edge?

By "almost parallel" I mean if set S of closing nodes has many conflicting edges with set T of closing nodes, then the edges between them are almost parallel. The emphasized edges in this ...
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24 votes
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Probability of defeating a dragon in one turn rolling a 20 sided die

In math class we were asked an optional problem I can't solve on my own: You are fighting a dragon with 250 hit points and are rolling a 20 sided die to deal damage. The dragon takes damage equal to ...
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Online time-series smoothing algorithm for sparse data

I am working on building a real-time system for processing and aggregating somewhat sparse and irregular survey measurements (ranges from 0-100, usually on the order of 20-100 measurements). I am ...
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What is the term for an algorithm that satisifies the bound $\lvert l(z,h_{s})-l(z,h_{s^{'}})\rvert \leq \beta$ where samples differ in one component

Consider $\mathcal{A}$ as an algorithm that satisfies the following condition for the loss function where $l$ represents some loss function and $z\in \mathcal{X}\times \mathcal{Y}$ is a sample and $s$ ...
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Interview question: communication by breaking windows [closed]

Here is an interview question for brainstorming: There are 100 different windows (suppose they stand in a line), 2 people, and 50 days. One communicates with the other by breaking windows in any day. ...
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Why does stochastic gradient descent lead us to a minimum at all?

Why do we think that stochastic gradient descent is going to find a minimum at all? I mean on each iteration SGD moves in the direction that reduces only current batch's error (SGD doesn't care about ...
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Clustering points in time series

I have time-series data - Temperature Vs Time. The temperature raises during regular intervals and stays high for some time before returning to a normal value as shown below. I would like to identify ...
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2 votes
2 answers
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What is the name of this algorithm for choosing which features to include in a neural network?

Once I read about one kind of neural network (for classification or feature selection) for a supervised training where you start with all inputs, then you proceed with a training step and randomly (or ...
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Show that the weights in IWLS algorithm are always 1

I have a data for fire insurance with a model with the responce variable Y claim size and there are two potential predictor. I have to fit the linear additive model $$E(log Y_i)=\beta_0+\beta x_{i,grp}...
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Understanding Variations of the Genetic Algorithm

I am working with the R programming language. I am trying to learn about different optimization algorithms such as the "Genetic Algorithm" (e.g. https://cran.r-project.org/web/packages/GA/...
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2 votes
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Is there an efficient algorithm to draw samples from these distributions?

Consider two-dimensional brownian motion, but in a maze, such that there are "walls" which prevent the path from taking certain steps (based on this tweet). I'm curious about algorithms to ...
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