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2
votes
1answer
32 views

Is there some theory of SVMs with infinitely many data?

I am trying to understand what does it means to have a (linear) SVM classifier (with soft margins) given the generative model of the data. And I realize I have not seen any paper on it, nor can I ...
0
votes
0answers
16 views

GB-RBM unable to learn and generate simple 2D-Motions?

I am trying to apply a GB-RBM to a variation of the LASA Handwriting Dataset to be able to generate new examples. My dataset contains motion trajectories of simple shapes like 'S' shapes, spirals, 'C' ...
0
votes
0answers
11 views

Has discriminative learning been studied more than generative learning?

It seems that there are far more discriminative learning approaches out there than generative. Have discriminative models been studied more in literature than generative? Thanks
1
vote
1answer
73 views

Example of how the log-sum-exp trick works in Naive Bayes

I have read about the log-sum-exp trick in many places (e.g. here, and here) but have never seen an example of how it is applied specifically to the Naive Bayes classifier (e.g. with discrete features ...
0
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0answers
49 views

How to implement a Gaussian-Rectified RBM and generate data?

I have a bit trouble implementing a gaussian-rectified RBM. I would like to show you how I would implement it. It would be nice if you point out errors or comment the implementation. These are the ...
1
vote
1answer
87 views

Discriminative vs. Generative Models

This has be asked before, but I still have not grasped it completely. I know that generative models model the feature distribution and that this includes modelling the P(x|y) and P(y), which are not ...
3
votes
2answers
96 views

Do discriminative models overfit more than generative models?

In an interview, the interviewer said that discriminative models tend to overfit more than generative models because they solve a more complex problem and hence consume more resources (or parameters) ...
0
votes
0answers
29 views

What is a forward/inverse model?

The term forward model comes up a lot when reading about Bayesian modelling. I am yet to understand what exactly is the forward model? Is it the model that describes the output/observed variable and ...
0
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0answers
41 views

Generative Models: Bayesian statistics hw help

Can anyone shed some light on this problem. Not looking for you to write out the answer for me, just some helpful hints that will hopefully lead me in the right direction. Here is the question: ...
2
votes
0answers
35 views

Discriminative learning and generative learning

I'm wondering why in Generative learning algorithm, they try to maximize the probability $\prod_{i=1}^np(x^{(i)}, y^{(i)})$ While in Discriminative learning algorithm, it is $\prod_{i=1}^np(y^{(i)} ...
1
vote
0answers
78 views

Difference between probabilistic generative, discriminative models and “discriminant functions”?

I have been thoroughly confused by a bunch of online resources regarding this issue. I would really like a simple explanation about the differences between generative and discriminative approaches, ...
2
votes
1answer
36 views

Is there a more precise definition of generative models?

Intuitively, a generative model is one that we can generate good data from. What I'm looking for is a formal definition of "good data." For example, for any classification model I could generate ...
2
votes
0answers
79 views

Treating multiple Dichotomies combined?

Let's say I am interested in choosing a new country $c_1, \ldots, c_k$ to live in. For some reason I can only apply to one country and only once. I know for each country a set of 2000 observations ...
1
vote
2answers
202 views

Topic Words Selection in Topic Modeling

I understand how generative model of topic modeling works; for each topic there is a distribution of words, and for each document there is a distribution of topics. Question is how words are ...
1
vote
0answers
68 views

Difference and connection between generative learning, discriminative learning and max-margin learning

I once heard that, generative learning, discriminative learning and max-margin learning can be separated in terms of their respective definition of loss function. I am not sure how to achieve that?
2
votes
1answer
161 views

Generative modeling of a mix of continous and discrete variables

I'm trying to build a generative model to run a Monte Carlo simulation. The existing data consist of a combination of discrete and continuous variables. Suppose I have a number of people... ...
2
votes
0answers
66 views

Statistical model of a website

I know that HMMs can be used to construct statistical models of text. Thus, we can generate text according to this model, and compute the likelihood of a text sample under the model. What tools are ...
0
votes
0answers
57 views

Generative model that penalizes clumping of data

I'm interested in modeling a generative process that encourages data to be "evenly distributed" over its support, i.e. clumping of data points is penalized. For example, if I have a mixture ...
4
votes
2answers
1k views

Why are Gaussian “discriminant” analysis models called so?

Gaussian discriminant analysis models learn $P(x|y)$ and then apply Bayes rule to evaluate $$P(y|x) = \frac{P(x|y)P_{prior}(y)}{\Sigma_{g \in Y} P(x|g) P_{prior}(g) }.$$ Hence, they are generative ...
18
votes
2answers
2k views

Generative vs. discriminative

I know that generative means "based on $P(x,y)$" and discriminative means "based on $P(y|x)$," but I'm confused on several points: Wikipedia (+ many other hits on the web) classify things like SVMs ...
2
votes
0answers
44 views

Generative modelling: what if the generating models have very different “quality of fit”

Say I want to classify my data into two categories. I am pretty sure that my data has been generated by two mixtures of Gaussians -- on has a bimodal and one a trimodal form. I then train the ...
10
votes
2answers
388 views

The connection between Bayesian statistics and generative modeling

Can someone refer me to a good reference that explains the connection between Bayesian statistics and generative modeling techniques? Why do we usually use generative models with Bayesian techniques? ...