Methods and principles of building "computer systems that automatically improve with experience."

learn more… | top users | synonyms

2
votes
1answer
258 views

How to combine the responses of two sensors?

I have two sets of responses from two different sensors. In each set, the first column is distance measured in feet, and the second column is the response of the sensor. Sensor A has response values ...
1
vote
1answer
19 views

Quantifying applicability domain for predictive models?

I have a big dataset and I want to build a classification model (svm, rf, ann etc.). Then I split the original dataset into training set and test set. I build the model using training set. After it ...
0
votes
2answers
207 views

Differences Between Logistic Regression in Statistics and in Machine Learning

I just found out that machine learning also has logistic regression as one of its methods. Can someone please tell me the differences between logistic regression in statistics and machine learning? ...
5
votes
2answers
176 views

Looking for a good followup to the Stanford AI course

I took the original Stanford AI course and got an immense amount out of it. I got a poor score since I did not have a great deal of time to work out all the problems.. But what I really enjoyed about ...
0
votes
1answer
79 views

Supervised learning : Hebb learning rule doubts

(A) In this book "Introduction to Neural Network using Matlab 6.0 - S. N. Sivanandam, S. N Deepa" ...
0
votes
0answers
10 views

Choice of loss function in correlation matrix prediction

There is a random vector $X=(X_1,\ldots,X_p)$, with $p$ large, $E[X]=0$ and $V[X_j]=1\ \forall \, j=1,\ldots,p$, but the correlations are different from zero. We cannot assume multivariate normality ...
7
votes
3answers
185 views

how to represent geography or zip code in machine learning model or recommender system?

I am building a model and I think that geographic location is likely to be very good at predicting my target variable. I have the zip code of each of my users. I am not entirely sure about the best ...
3
votes
0answers
28 views

What is the posterior probability of the data given the model used for model averaging with Bayesian Logistic Regression?

I am trying to learn about Bayesian Model Averaging using Bayesian Logistic Regression (Genkin, A., Lewis, D. D., & Madigan, D. (2007). Large-scale Bayesian logistic regression for text ...
0
votes
0answers
24 views

Feature Selection for look alike modeling using k-NN

I have a list of items and various parameters for each items. For each item on my list i need to identify items which are similar to the item from my whole population . I am planning on using K-NN ...
0
votes
0answers
23 views

Hidden Markov Models methods for selecting optimal number of states

Package RHmm (R) I have a vector which I fit into a hmm model in an attempt to select an optimal number of states for a hidden markov model. ...
1
vote
3answers
168 views

How to measure weight similarity?

I'm doing some machine learning and get a set of optimum weights in the end. I'd like to verify that these weights are by and large the same no matter how many times I train on the data. I assume that ...
0
votes
0answers
14 views

Facing Single-Class Training Set when Using Random Sampling

In a highly imbalanced binary classification (rare class < 10% of whole data), when I perform random sample selection (less than 15% of whole data to be selected for training) in a trial of 1000 ...
0
votes
1answer
34 views

tf-idf in multi-label classification task

I have a question regarding application of tf-idf. Let's assume I have a document classification task, there is a training set of documents that are multi-labeled, such that one document can have ...
0
votes
1answer
33 views

Measuring Statistical Significance of Binary Classification using Matthews Correlation Coefficient

Based on the following relationship between Matthew's Correlation Coefficient (MCC) and Chi Square: (MCC is the Pearson product-moment Correlation Coefficient) Is it possible to conclude that: By ...
2
votes
2answers
144 views

Variable selection with groups of predictors that are highly correlated

What variable selection approach should I consider if I have thousands of predictors with clusters that are extremely correlated? For example I might have a predictor set $X:= ...
3
votes
1answer
63 views

SVM primal formulation: does the constants constraint matter?

When finding the maximum margin separator in the primal form we have the quadratic program $$min\frac{1}{2}||\theta||^2$$ $$\text{ subject to: } y^{(t)}(\theta \cdot x^{(t)} + \theta_0) \geq 1, \ ...
0
votes
1answer
27 views

Using LDA in non-realtime twitter data

I try to understand user characterization from twitter data. How can I understand the user's interest from statuses? From my researches, LDA(Latent Dirichlet Allocation)suitable for topic extraction. ...
0
votes
0answers
23 views

Is a Gaussian-Gaussian RBM just a linear model?

The 'conventional' configuration of RBMs are Binary-Binary and Gaussian-Binary (and sometimes Binary-Gaussian) units. Although it is possible for both the visible and hidden units to be gaussian, ...
0
votes
0answers
23 views

Best way to classify a set through a single feature

I need to classify a single dataset through a numeric value. I added below a simple dataset to explain what I need. Restriction: Category has two values: 1 or ...
0
votes
1answer
94 views

machine learning model with 50% chance to be deterministic

I am trying to predict auction prices from a self gathered data set. I have titles available, and the price it was offered for. Now I noticed that whenever there is a 3 digit string of numbers in the ...
0
votes
0answers
12 views

Statistical tests to check quality of data before learning model

I have collected some gene expression data and I want to learn a Bayesian network out of it. Before that, I want to do some statistical analysis to test the quality of my data. Now I want to know ...
2
votes
2answers
92 views

improve precision in text classification

I am working on binary text classification using sklearn: The length of each sample is not high (~ 200-500 characters) I use TF-IDF to get important words as TfidfVectorizer(sublinear_tf=False, ...
1
vote
0answers
14 views

Learning to Rank: query-dependent vs. query-independent features

I've been doing some reading about learning to rank - specifically lambdaMART - and one thing I am confused about is the role of features. When training a model, should one only use query-dependent ...
0
votes
0answers
21 views

Information gain from a data set

I have a doubt regarding the use of information gain for data classification rather than conventional term frequency matrix.Lets assume I have 500 documents and I created a feature set of 12500*500. ...
0
votes
1answer
234 views

Error metric for a regression model with two dependent variables

I'm working on an algorithm that estimates two parameters of its input data. I have a representative set of samples with the true parameters, to act as a ground truth. As this algorithm uses a ...
1
vote
2answers
352 views

Test for linear separability

Is there a way to test linear separability of a two-class dataset in high dimensions? My feature vectors are 40-long. I know I can always run logistic regression experiments and determine hitrate vs ...
1
vote
1answer
29 views

Computational Complexity of Prediction using SVM and NN?

I've seen answers discussing the complexity of training SVMs and neural nets, but how about for predicting new responses once a model has been trained? For context, I'm working on an app that should ...
0
votes
0answers
19 views

Objective function of an SVM [on hold]

I have used the svm function in the e1071 package of R software to model my data using variables selected by my feature selection method. I have obtained predictions from this model using the ...
2
votes
1answer
41 views

Difference between Bayes network, neural network, Petri Nets and decision tree

What is the difference between Neural network, Bayesian network, Decision tree and Petri Nets eventhough they are all graphical models and visually depict cause-effect relationship. Thank you
5
votes
1answer
106 views

Why is optimizing a mixture of Gaussian directly computationally hard?

Consider the log likelihood of a mixture of guassians: $$l(S_n; \theta) = \sum^n_{t=1}logP(x^{(t)}|\theta) = \sum^n_{t=1}log\sum^k_{i=1}p_iP(x^{(t)}|\mu^{(i)}, \sigma^2_i)$$ I was wondering why it ...
1
vote
1answer
25 views

Hidden Markov Model: Predict observation sequence from state sequence

Given a transition matrix, starting probability, means and covariances Is it possible to predict the most likely observed sequence for a given state sequence using the above details? If yes, how? ...
5
votes
1answer
118 views

Selecting an appropriate machine learning algorithm?

I do not think that this is a difficult question, but I guess someone needs experience to answer it. It is a question that is asked a lot here, but I did not found any answer that explains the reasons ...
0
votes
0answers
20 views

Feature selection for one class SVM

I have around 300 features, i need to choose features for one class svm. can some one tell me the ideal algorithm for this use case. I know about that for feature selection regularised random ...
1
vote
0answers
19 views

HMM learning from video data?

I am having a problem understanding how to learn the parameters for the HMM from observed data. Let's say that my HMM model has one hidden variable for affect(emotion) with three values/states (anger, ...
2
votes
1answer
18 views

Why is the decision function for probabilistic models a quotient (when we only consider two models)?

Take for example, that we want to find the probabilistic model for only two document types (doc can be + or -). I was trying to understand why the way that we classify a document model was with the ...
3
votes
2answers
140 views

How to do multivariate machine learning? (predicting multiple dependent variables)

I am looking to predict groups of items that someone will purchase... i.e., I have multiple, colinear dependent variables. Rather than building 7 or so independent models to predict the probability ...
0
votes
1answer
48 views

Linear Regression Real Life Example

I am learning Machine Learning(Linear Regression) from Prof. Andrew's lecture. While listening when to use normal equation vs gradient descent, he says when our features number is very high(like 10E6) ...
0
votes
1answer
22 views

decision boundary of support vector machine when data is not linearly separable

Screenshot from this video: This describes the decision boundary of support vector machine as a optimization problem with two constraints. But it seems to assume that the data points are linearly ...
0
votes
1answer
32 views

Layers size in convolution NN

I have a pretty complex function that I'm trying to make my computer learn using CNNs. It involves 70 X 70 grayscaled images. The final output is the output of the last unit (it's because I want to ...
0
votes
1answer
20 views

Some Basic things we need to do when we are doing text classification

I am working on a project where I have to do multi-label text classification. I want to understand that whether my approach is correct or I am missing something. I am using R to do it. Clean ...
1
vote
2answers
115 views

Rescaling input features for neural networks regression

In Neural Nets for the regression problem, we rescale the continuous labels consistently with the output activation function, i.e. normalize them if the logistic sigmoid is used, or adjusted normalize ...
1
vote
1answer
39 views

Decision trees for advertising data

Assuming a dataset with the following attributes: Date (truncated), f1 ... fn, ...
1
vote
3answers
179 views

How to combine time-series based features with different frequencies

I have 3 features which I want to use in my classifier. They are all time-series data-based. However, they are all at different frequencies and there have different matrix dimensions. I was wondering ...
1
vote
2answers
45 views

Dealing with different time series data in Machine Learning

I am trying to create a stock market model based on fundamental variables for the US economy. I am using R. Some of the variables I am looking to include are: GDP, Unemployment Rate, Initial Claims, ...
1
vote
0answers
18 views

How to derive errors in neural network with the backpropagation algorithm?

From this video by Andrew Ng around 5:00 How are $\delta_3$ and $\delta_2$ derived? In fact, what does $\delta_3$ even mean? $\delta_4$ is got by comparing to y, no such comparison is possible ...
0
votes
0answers
25 views

Performance comparsion study on real data in MATLAB (machine learning)

I need to compare performance using 2-class classifiers–an LDA classifier and Exact Bayes in MATLAB. I have to use this dataset. Can anyone give me any advice how to do that (at least the steps of the ...
8
votes
1answer
304 views

Can a model of P(Y|X) be trained via stochastic gradient descent from non-i.i.d. samples of P(X) and i.i.d. samples of P(Y|X)?

When training a parameterized model (e.g. to maximize likelihood) via stochastic gradient descent on some data set, it is commonly assumed that the training samples are drawn i.i.d. from the training ...
0
votes
1answer
18 views

highly sporadic validation error during training with multilayer perceptron

I'm encountering an issue where a classifier I'm developing reports validation errors during training that span a wide range of values without consistently decreasing over time. Unfortunately, I'm new ...
-1
votes
1answer
21 views

Is it OK to increase validation checks and decrease min gradient while training neural network?

My input vector is a 130*85 matrix and my target vector is 130*26 matrix. I am using the below parameters for training the network with 60 hidden nodes. ...
4
votes
1answer
85 views

Explain steps of LLE (local linear embedding) algorithm?

I understand the basic principle behind the algorithm for LLE consists of three steps. Finding the neighborhood of each data point by some metric such as k-nn. Find weights for each neighbor which ...