2
votes
0answers
24 views

Handwriting Recognition - Percentage Match

I'm currently working on a senior project and we've chosen handwriting recognition. Initially I thought that using machine learning algorithms were a good idea for this, but after the thought below ...
0
votes
0answers
15 views

Which approaches are known to extract product / service data from previously unknown webpages via machine-learning?

To avoid reinventing the wheel, which approaches are known to extract product / service data from previously unknown webpages via machine-learning? Which keywords in a search engine might give me ...
0
votes
0answers
14 views

What is the latent class in pLSA

I want to implement Probabilistic Latent Semantic Analysis(pLSA) in Python. I have searched many times but couldn't find a simple tutorial. ...
3
votes
1answer
73 views

Evaluation of probabilistic predictions

In the 2010 KDD cup, participants were tasked with estimating the probability that a student would solve a particular exam question. The competition winner was whoever produced the lowest root square ...
0
votes
0answers
24 views

Density estimation using knn

I am dealing with sentiment results of web articles. Sentiment is represented with two int values, +ve and -ve. For given article "xyz" I am getting different sentiments while testing at different ...
0
votes
0answers
24 views

Expectation Propagation when the likelihood is already Gaussian

What happens with EP when the likelihood terms and the priors are already Gaussian. So, if we imagine that the posterior is given by: $$ P(\theta|y) = ...
1
vote
1answer
110 views

Expectation Propagation and multivariate priors

I have been struggling with this for weeks now. All EP examples that I have found on the net seem to deal with univariate priors and I am really at a loss as to how to make it work with a multivariate ...
0
votes
1answer
46 views

Bayesian Classification evaluation

I am trying to implement Bayesian Classification on the data set as follows: "Problem: classify whether a given person is a male or a female based on the measured features. The features include ...
0
votes
0answers
44 views

Intuitive understanding of Local Probability Distribution

I'm learning Bayesian network. I have problem in intuitive understanding of Local Probability Distribution. Can anybody explain to me what it is?
0
votes
1answer
84 views

Machine Learning on scarce data - how to tackle this task?

We are making algorithm for prediction of Conversion Rate (CR) for CPC advertising. We have historical (statistical) data that we can analyze in many projections (in many factors). For example we ...
2
votes
3answers
149 views

Machine Learning to Predict Class Probabilities

I am looking for classifiers that output probabilties that examples belong to one of two classes. I know of logistic regression and naive Bayes, but can you tell me of others that work in a similar ...
0
votes
1answer
102 views

Modeling time: Probability distribution over time?

I'm trying to model users' posting behavior during a day. Say we have a bunch of users, with the time they post tweets. Now, for each user, I would like to estimate the likelihood of he post a new ...
2
votes
2answers
159 views

How can we find the decision boundary for two overlapping continuous uniform distribution?

Say I have $X \sim \text{CUnif}(a, b)$ and $Y \sim \text{CUnif}(c, d)$. The parameters of $X$ and $Y$ overlap i.e., $a < c < b < d$. How can I find a decision boundary in such case? I am ...
2
votes
2answers
104 views

How to evaluate the quality of probability estimation?

I have a classifier that gives its decisions as probability estimations: for each datum it returns a set of probabilities $p_j$ for each known class $j$: $p_j(\vec{x})=P(c_{real}=j|\vec{x})$. I have ...
8
votes
3answers
460 views

What does a “closed-form solution” mean?

I have come across the term "closed-form solution" quite often. What does a closed-form solution mean? How does one determine if a close-form solution exists for a given problem? Searching online, I ...
2
votes
1answer
87 views

Naive Bayesian class probability greater than one

I have a problem with a simple Naive Bayes calculation. Given that I have an inbox, with the following characteristics: The mailbox contains 100 emails 50 emails contain the word “money”. 30 emails ...
1
vote
1answer
114 views

Why doesn't ML point estimate equal MAP point estimate even though I'm using uniform prior?

I asked a previous question about why the ML and MAP estimates are the same when using a uniform prior (How does a uniform prior lead to the same estimates from maximum likelihood and mode of ...
3
votes
1answer
174 views

Combine several softmax output probabilities

I would like to combine the outputs of five neural networks, each with a softmax output layer of three classes each. A typical, example output is shown below:- where Figure 1 is the output of model ...
1
vote
1answer
289 views

How does a uniform prior lead to the same estimates from maximum likelihood and mode of posterior?

I am studying different point estimate methods and read that when using MAP vs ML estimates, when we use a "uniform prior", the estimates are identical. Can somebody explain what a "uniform" prior is ...
31
votes
4answers
4k views

What does the hidden layer in a neural network compute?

I'm sure many people will respond with links to 'let me google that for you', so I want to say that I've tried to figure this out so please forgive my lack of understanding here, but I cannot figure ...
0
votes
1answer
58 views

Comparing two points corresponding to two different normal distributions

I have two multi variate normal distributions N1 and N2. Say two points p1 is from N1 and p2 is from N2. I want to get some statistical features from these two points. How can I do it? I need a ...
0
votes
2answers
80 views

Estimating probabilities using Bayes rule?

I am working on a past exam paper. I am given a data set as follows: Hair {brown, red} = {B,R}, Height {tall, short} = {T,S} and Country {UK, Italy} = {U,I} (B,T,U) (B,T,U) (B,T,I) (R,T,U) (R,T,U) ...
0
votes
0answers
83 views

How to calculate entropy in log Scale?

I'm working on a problem , where a function returns the Log probability of P(X=x) . Now I would like to find the Entropy of X. But since the probabilities I get are log probabilities, again taking its ...
2
votes
0answers
174 views

Kullback-Leibler vs Hellinger Distance

I am working on this problem in which I have a dataset of n-dimensional examples that come from different and unknown distributions. Given a new sample, I wish to find k examples from the dataset that ...
2
votes
0answers
89 views

Random sampling for estimating mutual information - Time complexity and sampling error?

I have a dataset and I want to compute the mutual information (MI) for a selected set of variables. The dataset is large enough so that computation of the MI may take undesirably long time. Can I just ...
1
vote
1answer
261 views

Combining results of two classifiers to better classify a data-point

Suppose that a classifier A classifies a data-point $x$ in class LA1 with probability PA1 and class LA2 with probability PA2 (with LA1 != LA2 and PA1>PA2); and that a classifier B classifies a ...
2
votes
4answers
775 views

Methods & CRAN packages to predict probability using neural networks or others machine learning algorithms

I have a medical database containing 7 input variables (4 are binary) and a binary outcome variable (Survival: yes/no). My objective is to train and test an algorithm that predict probability of ...
3
votes
0answers
31 views

Maximizing choice

There are N number of people and X amount of objects with different values. Each person will choose an object and will obtain that object's value. If multiple people choose the same object then the ...
7
votes
1answer
156 views

Confusion related to semisupervised learning in random walk

I am trying to understand the semi supervised learning in random walk. Lets say I have 10 classes and I have some labelled and unlabelled points. Now, I need to find the labels for the unlabelled ...
0
votes
0answers
83 views

Binary classifier probability measure?

I've the following situation. I've a binary classifier which classifies input feature vectors into either of two classes 'y' or 'n', along with the probability of it being in either of the classes ...
1
vote
1answer
348 views

How to convert multiple ranking scores into a probability distribution?

I would like to create a topic distribution for a document. The current model I am trying to implement is: for each sentence in the document, I am getting a topic assignment with a score, e.g. "1st ...
6
votes
1answer
272 views

Getting probabilities over 1 in positive and unlabeled learning

I have a question regarding PU-Learning, which deals with learning from positive-labeled (no labeled negative examples) and positive/negative-unlabeled data. Particularly, my question is about the ...
1
vote
1answer
87 views

Probability of observed data in HMM

In a given Gaussian mixture model with observed continues variables $Y$ and latent discrete variables $X$ I want to apply the forward-backward algorithm in order to compute the marginal posteriors ...
15
votes
2answers
435 views

Combining classifiers by flipping a coin

I am studying a machine learning course and the lecture slides contain information what I find contradicting with the recommended book. The problem is the following: there are three classifiers: ...
7
votes
1answer
117 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 ...
1
vote
1answer
281 views

pLSA - Probabilistic Latent Semantic Analysis, how to choose topic number?

I am learning about pLSA (Probabilistic Latent Semantic Analysis) right now, in the hopes of being able to apply it to biomolecular annotation prediction. I have a very simple question: How do you ...
0
votes
0answers
101 views

having trouble applying hidden markov models to my game [duplicate]

Possible Duplicate: having trouble applying hidden markov/machine learning models Happy New Year! I’m having a problem applying hidden Markov models to a game I’m building to learn about ...
6
votes
2answers
348 views

Learning to create samples from an unknown distribution

I am interested in new generating samples to approximate some unknown distribution X, where each new sample is a real-valued vector. The purpose it to be able to create a new (arbitrary large) ...
0
votes
1answer
190 views

How to compute mean vector and covariance matrix of equal distributions?

This question is an extended version of this one. As you can see here, two distributions are equal, I need to compute the parameters a,b,c,d and e. Could you show me a way to do that? Assume a ...
2
votes
1answer
156 views

How to get started in data-relation algorithms and mathematics?

I am very interested in the concepts and discussions taking place here. However, I'm not entirely sure what this field of study is called or what the many branches of study are being discussed here ...
1
vote
2answers
361 views

Metric for probability based classification

I am doing a system for classifiying documents. The project demands the use of probability based output. So a sample will have a probability for belonging to each class. For now I use logistic ...
4
votes
1answer
272 views

What is the $i$th sufficient statistic in the EM algorithm for Gaussian mixture models?

I am reading up on the EM algorithm for Gaussian Mixture Models, and there is consistent reference to the $i$th sufficient statistic. What is this, and why is it relevant to the algorithm?
9
votes
1answer
1k views

What is the best way to learn the fundamentals of probability required for machine learning algorithms?

I took a probability course in university a few years ago, but I'm going through some machine learning algorithms now and some of the math is just befuddling. Specifically right now, I'm learning ...
5
votes
2answers
401 views

Deriving mathematical model of pLSA

After knowing how LSA works, I went on continue reading on pLSA but couldn't really make sense of the mathematical formula. This is what I get from wikipedia (other academic papers/tutorial show ...
3
votes
1answer
136 views

White box machine learning probability estimator

Have you ever heard about Child-Pugh cirrhosis score? There are five features, each feature is discretized in three intervals. For each interval you get a point, eg 1 for the first, 2 for the second ...