Questions tagged [machine-learning]
Machine Learning is a subfield of computer science that draws on elements from algorithmic analysis, computational statistics, mathematics, optimization, etc. It is mainly concerned with the use of data to construct models that have high predictive/forecasting ability. Topics include modeling building, applications, theory, etc.
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AdaBoost implementation and tuning for high dimensional feature space in R
I am trying to implement the AdaBoost.M1 algorithm (trees as base-learners) to a data set with a large feature space (~ 20.000 features) and ~ 100 samples in R. ...
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Training value neural network AlphaGo style
I have been trying to replicate the results obtained by AlphaGo following their supervise learning protocol. The papers specify that they use a network that has two heads: a value head that predicts ...
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How to predict advantage value in deep reinforcement learning
I'm currently working on a collection of reinforcement algorithms: https://github.com/lhk/rl_gym
For deep q-learning, you need to calculate the q-values that should be predicted by your network. There ...
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differences between LSQR and FTRL when working with very sparse data
I have a 2M instances dataset with millions of very very sparse dummy variables created using the hashing trick = ...
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What does big O mean in KNN optimal weights?
Wiki gives this definition of KNN
In pattern recognition, the k-nearest neighbors algorithm (k-NN) is a
non-parametric method used for classification and regression. In both
cases, the input consists ...
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Predicting change of shapes/coordinates
I'm trying to find a way to predict/calculate how a shape (e.g. outline of a glacier) will change in the future—based on its history (previous shape) and additional factors (e.g. Δtemperature).
In my ...
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Confidence value in AdaBoost?
I read this introduction about AdaBoost (http://www.cs.man.ac.uk/~nikolaon/~nikolaon_files/Introduction_to_AdaBoost.pdf), and am curious why confidence for each model is defined as
$$\alpha_j=\frac{...
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Classify driver based on time-series sensor data
I want to build a model that can detect which driver is driving now the car based on a dataset that contains 20 driver records for 3600s each driver ( the dataset contains all the car sensors values ...
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how to propagate error from convolutional layer to previous layer？
I've been trying to implement a simple convolutional neural network. But I've been stuck at this problem for over a week.
To be specific, assume there are 3 layers in a convolutional pass, marked as ...
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Difference Between Attention and Fully Connected Layers in Deep Learning
There have been several papers in the last few years on the so-called "Attention" mechanism in deep learning (e.g. 1 2). The concept seems to be that we want the neural network to focus on ...
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What ML architecture fits fixed length signal regression?
My problem is of regression type -
How to estimate a fish weight using a fixed-length signal (80 data points) of the change in resistance when the fish swim through a gate with electrodes (basically 4 ...
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Dealing with categorical variables in Isolation Forest
Isolation Forest is widely used when dealing with outlier/anomaly detection when we have no labels. The theory behind is that making random split at random points and counting how many splits you do ...
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Time horizon T in policy gradients (actor-critic)
I am currently going through the Berkeley lectures on Reinforcement Learning. Specifically, I am at slide 5 of this lecture.
At the bottom of that slide, the gradient of the expected sum of rewards ...
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Evaluation of regression models with different evaluations (MSE, variance, VAF etc.)
When comparing several regression models in terms of quality, it seems like most have agreed on the MSE.
There are also papers comparing "variance" and "variance accounted for (VAF)&...
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Illustrating the dimensionality reduction done by a classification or regression model
Tl;DR: You can predict something, but how do you explain the prediction?
EDIT: I have built a website that tries to answer this question with means of embedding / visually clustering data according ...
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