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16 views

Difference between a generative MRF and discriminative CRF

I am having trouble developing the intuition behind the difference between a regular generative Markov random field (MRF) and its discriminative counterpart. So, as I think I have understood so far ...
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1answer
35 views

Latent Dirichlet Allocation - definitions

I am self-studying the article on LDA by Blei, Ng and Jordan (https://www.cs.princeton.edu/~blei/papers/BleiNgJordan2003.pdf). at the start of the second section - the following definitions are given: ...
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0answers
14 views

Gaussian classifier: if two gaussians have equal variance is it possible for them to produce a non-linear decision boundary?

I have been playing with this a bit and I don't believe they can. However, I am very new to machine learning and my maths isn't strong enough to be certain.
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0answers
18 views

How alignment between words are generated by IBM model1?

I implemented the translation model IBM1. As a result, I got the translation table P(targetWord|sourceWord) wich is ok. I want also to obtain the alignment of words in the corpus that I used in order ...
3
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1answer
140 views

How do you evaluate a generative model?

Evaluating a discriminative model is relatively easy: compare the predictions with ground truth, using cross-validation. Unfortunately this strategy can't be used for generative models. Surely this ...
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0answers
33 views

Use cases for P-Kernel for SVMs

I've been reading the book Kernel Methods for Pattern Analysis by Shawe-Taylor and Cristianini (2004), where generative kernels (like p-kernel and fisher-kernel, not to be confused with polynomial ...
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0answers
140 views

What enforces features diversity in RBM?

I'm working on an implementation of a Restricted Boltzman Machine (RBM). I made some tests on the MNIST dataset trying to learn a representation of the digit 2. My inputs are binary images. My aim is ...
2
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1answer
40 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 ...
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1answer
474 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 ...
2
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1answer
181 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 ...
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2answers
340 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) ...
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0answers
46 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
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1answer
74 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)} ...
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0answers
131 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
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2answers
58 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
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0answers
81 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 ...
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2answers
232 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 ...
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0answers
76 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
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1answer
227 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... ...
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0answers
67 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 ...
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1answer
144 views

Beyond Fisher kernels

For a while, it seemed like Fisher Kernels might become popular, as they seemed to be a way to construct kernels from probabilistic models. However, I've rarely seen them used in practice, and I have ...
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0answers
64 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 ...
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2answers
2k 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 ...
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2answers
5k 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
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0answers
47 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 ...
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2answers
486 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? ...