Questions tagged [generalization]

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My CNN is not learning but just memorizing [duplicate]

So there has been similar posts but none of them solves my problem, so I decided to created a new question. I'm working on a regression project where I intend to use CNN to predict material properties,...
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Is there a name for this "generalized PCA"?

I was trying to understand the difference between Principal Component Analysis (PCA) and Canonical Covariate Analysis (CCA), and noticed that if you write them down to look as similar as possible, ...
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Clarification of line in proof of consistency theorem (Vapnik)

In Vapnik's Statistical Learning Theory (1998 edition) on pages 89-92, he proves a "key theorem of learning theory" that states the conditions for when: "the following two statements ...
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Consequences of paired healthy and diseased samples in machine learning

Consider a set-up in which we are using machine learning to classify between healthy and diseased samples. Obtaining the data requires some invasive procedure - therefore all the healthy samples come ...
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Overfitting, generalization, data augmentation, regularization, how do they relate to each other? How to measure?

Recent work such as Deep Double Descent shows that overfitting is not really a problem with large models, even without any data augmentation or regularization (L2 weight norm, dropout or so). Edit: Ok,...
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Rule of thumb for removing / keeping attribute based on occurrence frequencies among training observations?

I have a training dataset expressed with binary values, where 1 indicates an attribute is used in an observation, and 0 indicates it is not. I was wondering if I should remove an attribute from the ...
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Is it possible to know whether a linear SVM is overfitting from the features' weight and value distribution in training?

I have a text sentiment classification model trained using linear SVM on 2500 training instances with around 14000 features(word), every sample is represented as binary vector with 1 indicate presence ...
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Is there a multivariate joint Amoroso distribution?

The Amoroso distribution is a remarkable feat of abstraction as it exactly or asymptotically generalizes dozens of named probability distributions. Is there a published/pre-published treatment of ...
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External loss functions for Spectral/Density-based clustering

In this article, Abou-Mustafa and Schuurmans proposed a method that makes it easy to decide what unsupervised learning algorithm generalizes 'better' to the entire dataset. In particular, this needs ...
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domain generalization vs distribution generalization

Is there any difference between domain generalization, distribution generalization? How to differentiate these two. domain generalization is applied to an unseen domain distribution generalization is ...
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What are the tradeoffs of using the generalized $f$-median?

The generalized f-mean is a generalization of multiple estimators, and even generalizes the generalized mean. For some invertible function $f$, and $k$-dimensional vector, it is given as: $$M_f(\vec{x}...
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Mean of Generalization of the Dirichlet Distribution

I know that if $X_{1},X_{2},...X_{n}$ are independent $\mathrm{Gamma}(\alpha_{i},\theta)$ - distributed variables (notice they all have the same scale parameter $\theta$) and $Y_{i}=\frac{X_{i}}{\sum_{...
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What does one imply by the term "overgeneralization" in machine learning?

I know overfitting and underfitting in machine learning context, and what generalisation means as well. But, recently I was introduced to an uncommon terminology "overgeneralization" in ...
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Metafor Package: How to conduct Meta Regression with reliability generalization

How to conduct meta regression in "metafor" after I got I2 heterogenity 94%. My study reliability generalization alpha Cronbach, with continuous and categorical moderator variable. Thanks ...
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Cannot achieve generalization of machine learning model

I'm working on a balanced, binary classification problem in a time-series (financial) dataset. I am using K-fold cross validation that is adapted for time-series (so that I'm never using future data ...
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2 answers
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SVM Model: What's a healthy number of support vectors?

For a SVM model what is a healthy number of support vectors? or more precisely what's a good ratio of number of support vectors to the total number of training samples, 10%, 20%, 30%, 50% ... 80%? Is ...
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Model selection based on "generalization set"

I'm working on a very general problem in machine learning where I have to choose from different models or from updates which would lead to different models. A first idea was to define a "...
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Does up-sampling lead to lots of false positives in production?

Say we have a dataset with a binary outcome variable that takes the positive case (outcome = 1) roughly 20% of the time. Often, we would modify the training set by ...
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Role of misspecification by biased data in the generalization error

I am confused with the role that model misspecification plays in the generalization error, in particular when the misspecification is due to a biased (non representative) training dataset. To clarify ...
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Intuitive explanation on "Generalization " [closed]

I recently worked on Generalization of Gradients. If I'm asked to find Generalization of Gradients or for Dirichlet distribution, etc. I'll do it correctly like a machine. But I didn't understand it. ...
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2 votes
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Statistics terminology: $n$-way and $m$-sample

In statistics, I see certain things described by "$n$-way" or "$m$-sample." For example, there is "$n$-way" ANOVA for any $n$ and "$m$-sample" t-tests for $m=1,2$. I want to get a handle on what these ...
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