# Questions tagged [online]

Online algorithms refer to computations that are performed iteratively, with data arriving during the computation. For questions focusing on the Internet, please use the "internet" tag.

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### Efficient online (rolling window) estimation of a GARCH model

I have a time series $x_t$ of length $n$. I would like to model it using rolling window approach with window length (width) $w$: window $1$: $x_1,\dots,x_w$, window $2$: $x_2,\dots,x_{w+1}$, $\dots$, ...
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### Reducing the dataset size for KDE

I have GPS data, so 2 coordinates, and I want to estimate the busiest places (i.e. the places with more data points). However, I have a lot of points: currently ~4 million for 12 days, and I will be ...
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### Can Incremental/Online Learning be Implemented for Custom Word Embeddings

I'm currently working with a neural network (in Keras) that predicts classes from text using custom word embeddings. It's worked well until now, but has to be retrained frequently on new data. The ...
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### How come Mini Batch K means partial_fit method be useful for stream clustering?

Currently, I'm studying the advance in cluster analysis regarding stream clustering. I ended up assessing Mini batch K means because of some comments I read on the Internet, like the following one: ...
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### Batch computation vs online computation

In short, could you explain in which situations online computation is better than batch computation? (I am currently reading a paper (https://arxiv.org/abs/1003.0120) about offline policy evaluation ...
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In the online convex optimization literature static regret is defined as $\sum_{t=1}^{T}\left(f_t\left(x_t\right)-f_t\left(x^*\right)\right)$ where $x^*=\arg min_{x\in\mathcal{X}}\sum_{t=1}^{T}f_t\... 1answer 75 views ### General process of training a Neural Network [closed] I have a very broad question about the general procedure of training a NN. I am not too concerned about the precise algorithms in this question at the moment. But there is one thing bothering me. ... 1answer 36 views ### What is the cumulants of a whole data in terms of the cumulants of its parts? I have around 8 billion data points, and I need to calculate the distribution and the cumulants of this distribution. However, due to technical restrictions, and time constraints, I can only ... 1answer 86 views ### Can you use stochastic gradient descent with a multinomial likelihood? I have a multinomial likelihood of the form: $$P(\underline n|\underline x) = N!\prod_{i=1}^M \frac{f_i(\underline x)^{n_i}}{n_i!}$$ where$\underline x$is a vector of parameters,$f_i(\underline x)...
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I encountered this statement in multiple lecture slides I found through duckduckgo, but I found no proof, and it doesn't seem trivial for the general case. Can anyone verify the $log(T)$ regret bound ...
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### Online update of Pearson coefficient

Suppose I have an online stream of data points $x_i,y_i$, where $i=1,2,\dots$. I want to compute the Pearson correlation coefficient between the vectors $\vec x$ and $\vec y$. But here is the catch. ...
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### Resources for Non-Bayesian Online Change-point Detection

I am interested in learning about the fundamentals of on-line change point detection. I am specifically not interested in the Bayesian methods. The only solid resource / review / survey I could find ...
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### Probability of Incurring Maximum Loss

In online classification one can use mistake bound learning, where one assumes that all $y$ are generated by some target mapping $h^*: \mathcal{X} \rightarrow \mathcal{Y},\,\, h^* \in \mathcal{H}$. ...
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### Testing methodology

I recently built a simple feed forward NN to predict daily demand (48 output neutrons, representing half hours) based upon 32 input features. I tested the performance by firstly doing 10 fold cross ...
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### Is there a goodness of fit metric that can be computed online with $O(1)$ memory?

Say I have two random streams of two dimensional data. I want to measure how closely their underlying PDF's match. My current method is to estimate the PDF's by accumulating the samples online in a ...
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### Dynamic Calculation of Variance after removing an observation

I know that there are formulas for calculating variance in dynamic fashions. I have also seen formulas for calculating rolling variances assuming you know what number is leaving and entering the ...
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### Is there a name for this? Updating class probabilities, online

An object is of an unknown class $y$. We receive a stream of measurements $x_1, x_2, ...$ of the object. Every time we receive a new measurement, we want to update our estimate of the class label $y$ (...
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### Recursively updating the MLE as new observations stream in

General Question Say we have iid data $x_1$, $x_2$, ... $\sim f(x\,|\,\boldsymbol{\theta})$ streaming in. We want to recursively compute the maximum likelihood estimate of $\boldsymbol{\theta}$. That ...
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### what is the diffrences between online and one pass learning?

as long as I know, online learning takes actions at each time step (for each data), and one-pass algorithm just can see each data once. I already read Wikipedia: about streaming algorithms. These ...
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### How to use/treat a hidden layer as the new target to predict in a neural network?

Let's suppose I have a neural network with one hidden layer. During training, for a given pair of (input, target), I want to perform several iterations, such that the first iteration would be trying ...
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### What test to use to find the probability of highest value?

If I have a vector of around 40 values each with a normally distributed error, is there an easy way to figure out the probability of each element being the element with maximal true value? For context,...
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### Online estimation of drifting discrete probability

I recently come across (in a practical setting) to the following problem. Suppose I receive items from a finite set ,one at a time . At each moment one item is drawn independently from an unknown ...
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### Performance of Hierarchical Temporal Memory on unsupervised online anomaly detection problems

I'm looking for an algorithm to detect anomalies in streaming data (server metrics). The detection needs to be near-real time and unsupervised (labeled data will never be available, unfortunately, and ...
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### Adding new center in an RBF network without memorizing previous training examples

Suppose we train an RBF by minimizing the LSE on a couple of training points and we are doing it incrementally in an online fashion. So basically we update the QR factorization using e.g. Givens ...
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### Algorithm for selecting largest possible value, when observing online sequence of unknown distribution?

I have been trying to devise an algorithm for a problem that's been bugging me for a while. For some weird reason I haven't been able to find any mention of this problem in the literature, so far. I ...
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### Effects of exclusion on averages

Given a set of data points, if a single point which is above/below the average is excluded, can it be said that the new average will surely decrease/increase? What if the average is the median instead ...
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### Extended kalman filter vs online passive-aggressive

I was wondering, what are the advantages and disadvantages of extended Kalman filter and online passive-aggressive algorithm when we use them to train our networks. I have RBF neural network and I'm ...
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### Are ensemble learning methods for data streams restricted to online or batch learning?

Recently I'm working on some online learning algorithm (using RBF neural network ) for classification. As I read papers in this area I found there is an issue in online-learning called concept drift ...
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### Real-Time signal processing with RandomForestClassifier in sklearn always predicts one class

I am trying to perform real-time decision making on data from a radar sensor and trying to detect occupancy. I generated data using the same sensor annotated it manually as vacant or occupied. I ...
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### What data format should I use to learn the nonlinear output behavior of my guitar distortion pedal using a neural networkl?

My Problem I've built a very simple transistor guitar pedal. it has 1 mono input, 1 mono output. Now, all I have ever done in the past with ANN's is offline learning with labelled data and some work ...
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### Gradual clustering in deterministic manner

We have 128-dimensional vectors representing people's identities where the euclidean metric defines the similarity between them. Ours solution requires them to be clustered and then annotated (assign ...
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### What are online learning and offline learning in the context of reinforcement learning? [duplicate]

In this question, many users have discussed online and offline learning in machine learning. But, in the context of reinforcement learning, what are exactly online and offline learning?
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### Updating of OLS regression estimates when one data point is changed

How do I estimate the new slope and intercept if I'm given a 'updated' data instance? For example I have a regression equation y = 1x + 0.5 and this is learned with a data set of 10 data instances ...
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### Online algorithm for the mean square error

Given a dataset $\{(x_1, y_1), (x_2, y_2), \dots\}$, we can compute incrementally (or "online") the linear regression for those points. In other words, given a new point $(x_i, y_i)$, we can ...
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### Online deseasonalization of time series data

Are there any existing methodologies of deseasonalizing time series data online, in order to avoid lookahead bias? It seems that if you don't deaseasonalize time series data online, you would not be ...