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

learn more… | top users | synonyms

0
votes
0answers
6 views

In LibSVM, svm-scale gives data that is all 1 and -1 [migrated]

As is described in the title, when I try to use svm-scale to scale my regression data into [-1, 1], the scaled data is all 1 or -1. I've confirmed that the original data itself has no problem. I'm on ...
-2
votes
0answers
14 views

What are the applications of multi-agent approach for large scale data mining

I understand that some large scale data mining can be done via grid computing. So I am wondering if there is any advantage of employing multi-agent approach in this area. Any references or resources? ...
0
votes
0answers
20 views

Prediction using Support Vector (SV) method in R

I came to know that using SVM method we can predict the future value more accurately than other normal methods (like ARIMA). My question is how do we give the future index value (let's say 101 when we ...
0
votes
0answers
16 views

GBM, it's overfitting/multicollinearity problem and parameter setting up

I recently came across a predicting problem (0-1 outcome, with more than 80 variables), I decided to use GBM (Gradient Boosting Machine by Friedman)to handle this job. I let the GBM use only 70% of ...
0
votes
0answers
15 views

Removing labelling noise

I have a big data set with unlabelled observations (several million) and about 20 thousand properly labelled ones. There are only two classes and all correctly labelled samples belong to the same ...
0
votes
1answer
23 views

mixing binary and real-valued features with SGD

I'm going to be using a logistic regression model and using SGD to determine the feature weights. Is it OK for me to use a mix of binary and real features, without doing anything like scaling or ...
0
votes
1answer
12 views

Feature selection with a binary dependent variable

Given we have a binary dependent variable and 100s of features and ~50k observations, is there a generally accepted way to trim the features via some type of machine learning concept? I was trying a ...
0
votes
0answers
11 views

Restricted Boltzman Machine Non-Hidden Layer Approach

An RBM is defined by the joint probability distribution $$p({\bf x},{\bf h})=\exp(-E({\bf x},{\bf h}))/Z$$ where $$E({\bf x},{\bf h})=-{\bf h}^TW{\bf x} - {\bf c}^T{\bf x} - {\bf b}^T{\bf h}$$ ...
1
vote
0answers
11 views

Learned production test

To validate the acoustic performance of a product, we are using hand-engineered features and thresholds. Everytime a new hardware problem arises we have to at least tweak a parameter and at worst add ...
9
votes
2answers
613 views

What does the name “Logistic Regression” mean?

I am checking an implementation of Logistic Regression from here. After I reading that article, it seems the important part is the find the best coefficients to determine the sigmoid function. So I ...
0
votes
1answer
26 views

Calculating the information gain on the features with python

I'm looking for a python library that computes the information gain for the features given a training matrix. Are you aware of any?
0
votes
0answers
21 views

semisupervised classification training on all or part of unlabeled data

I have 3 sets of data. A positively labeled dataset. An unlabeled dataset that has for sure positive (around 75%) and negative data. An unlabeled dataset that has for sure positive data and maybe ...
2
votes
2answers
77 views

How to build a prediction model for exam score based on previous scores

I am trying to construct a formula, which will take student's previous exam results (for ex: SAT) taken at particular dates and predict his future test result. One X is previous test result 1; ...
1
vote
2answers
107 views

Multivariate Bayesian formula

I got there example graphs bishop's PRML (8.2.1) 1. a <- c -> b $$ p(a,b,c) = p(a|c)p(b|c)p(c) --(1)\\ p(a,b) = \sum_c p(a|c)p(b|c)p(c) --(2) $$ Q1: Can I use a new graph to represent the ...
1
vote
1answer
23 views

Pairwise compatibility metric

I work at a company that sells clothes, and I've had good results with using cosine similarity to determine which products are "similar" to each other simply based on who owns them. I wanted to take ...
0
votes
0answers
21 views

how to measure the transform ability of a query on Internet Advertising

I have a task which aims to measure the transform ablity of a keyword , the transform ablity can be understood as the probability of a query on the search engine brings an order to the customers.For ...
0
votes
1answer
29 views

How to understand the Gaussian Progress Regression?

I know Gaussian Progress Regression is completed determined by its mean and covariance functions. For given training and test data, I can compute the covariance matrix correctly like a machine, and I ...
0
votes
0answers
8 views

Calculating the Similarity of Survey Responses

I was wondering if anyone had experimented with different functions for calculating the similarity of two sets of survey responses. I am going to be plugging it into a hierarchical clustering algo and ...
2
votes
1answer
40 views

Model for probability of song reaching top 10 ranking, over time?

I'm trying to model the probability of a song reaching Billboards top 10 over time. My data has the columns "Day since release", "If reached top 10". For example, [12,1] means the song hit top 10 on ...
0
votes
0answers
16 views

Confusing related to kernel method of Regression

In the Support Vector Machine (SVM), non-linear data are mapped to a higher-dimensional feature space to be separated linearly, a kernel is used to compute the inner product in the lower dimensional ...
0
votes
3answers
91 views

Is Maximum Likelihood Estimation (MLE) a parametric approach?

There are two main probabilistic approaches to novelty detection: parametric and non-parametric. The non-parametric approach assumes that the distribution or density function is derived from the ...
0
votes
0answers
19 views

Learning theory for search data?

Has learning theory ever been applied in practice for search log data? If so, what are some findings about generalization/learnability from this data? I'm interested in generalization about an ...
0
votes
0answers
20 views

Custom Neural network in matlab

I am trying to make a custom Neural network structure using 'network' command. I am a little confused.Can we change the connections between individual neurons?Like it is possible in weka?
0
votes
1answer
21 views

How to get both MSE and R2 from a sklearn GridSearchCV?

I can use a GridSearchCV on a pipeline and specify scoring to either be 'MSE' or 'R2'. I can then access ...
1
vote
1answer
64 views

Support Vector Machine Question

I need help with the following problem. I provided my current (partial) solution, and I hope someone can correct me and/or give me suggestions as to how I should solve the parts that I've left out. ...
0
votes
0answers
38 views

MATLAB interperetation of Neural networks

I am new to Neural networks and I am trying to build a custom neural network using the NN toolbox in MATLAB.I am using the "create custom neural network function". Now, I find the neural network ...
8
votes
3answers
2k views

Why do people like smooth data?

I am to use the Squared Exponential kernel (SE) for Gaussian Process Regression. The advantages of this kernel are: 1) simple: only 3 hyperparameters; 2) smooth: this kernel is Gaussian. Why do ...
2
votes
1answer
99 views

Some questions on Principal Component Analysis (PCA)

I am trying to understand some descriptions about PCA (the first 2 are from Wikipedia): 1) Principal components are guaranteed to be independent only if the data set is jointly normally distributed. ...
0
votes
0answers
16 views

Extraction of a decision boundary (LDA) after a systematic querying of the feature space and convolution with Sobel filter (examples in numpy)

I am doing some experiments with LDA (Linear Discriminant Analysis), in python. Now I am at the point in which I would like to display the separation planes in the 3-dimensional feature space. I ...
0
votes
0answers
8 views

S and N parameters of Ridor

I am using WEKA and in particular their implementation of Ridor. The documentation says this about the parameters S and N: -S Set number of shuffles to randomize the data in order to get better ...
2
votes
1answer
41 views

Differences in AUC calculation between pROC and ROCR

Does anyone know the difference in calculation between these two AUC packages? They get different results when I add in positives with predicted value of 0 (simulating a prob model where many outputs ...
1
vote
0answers
30 views

PCA + L1 equivalent to elastic net?

I am performing a logistic regression on a rather big dataset (700k+ samples and 1k+ features). I suspect that a lot of these features will be highly correlated and multicollinearity can be an issue. ...
6
votes
2answers
147 views

Clustering a noisy data or with outliers

I have a noisy data of two variables like this. ...
1
vote
1answer
101 views

What are basic differences between Kernel Approaches to Unsupervised and Supervised Machine Learning

I got nice graphical representation of Machine learning for clustering / classification. Source: Kernel Approaches to Unsupervised and Supervised Machine Learning by Sun-Yuan Kung Here are my ...
0
votes
0answers
9 views

When to cluster features for supervised learning?

I'm doing a project on dog adoption patterns, and I realized that there are many (100 +) different breeds of dogs. I'd like to build a predictive model using breed as covariate, but I'm not sure ...
3
votes
1answer
55 views
+50

Estimating the time for completing a sequence of actions

In short: suppose I have observations for times taken to do some action. I want to estimate, how long will it take to complete a sequence of actions. The estimate should minimize the mean absolute ...
4
votes
2answers
45 views

When do kernel based method perform better than the regular

I am used with linear models. I can see rising use of kernel based method particularly in machine learning. The following is an example Gaussian kernel using ...
0
votes
0answers
35 views

Neural-Net style pattern recognition with an unknown/varying number of inputs?

Say for example I had a weighted graph such that each node had an associated value. The nodes' values are given by some function of the edge weights and the number of edges as well as the node's ...
1
vote
0answers
24 views

Low pass filter to maintain edge information

I am looking for a kernel as low pass filter that satisfy as:I must find a kernel that statisfies as follows: In the my reference paper, the author suggest gaussian kernel that is The gaussian ...
0
votes
1answer
32 views

kernels and similarity (in R)

I am trying fit different kernels to calculate similarity matrix in R. Here is example data - X matrix : ...
0
votes
1answer
45 views

How to select kernel for Gaussian Process?

In Gaussian Process (GP), the kernel (co-variance function) is used to measure the similarity between one point and a given point. There are so many kernel functions for GP, and I wonder how to select ...
0
votes
2answers
30 views

Dummy Variables and Learning algorithms

Suppose a predictor variable $x$ is nominal/categorical with three levels: $1,2,3$. Thus we create two dummy variables $x_2$ and $x_3$ with level $1$ as the reference variable. Let $y$ be a binary ...
1
vote
0answers
71 views

Machine Learning Process for detecting edges of overlapping objects with OpenCV

I'm quite new to machine learning and a bit unsure about the whole process and the interpretation of the results. The Task: I have images with some objects of somewhat the same color and shape which ...
2
votes
1answer
33 views

full batch v.s. online learning v.s. mini batch

This is a question from a coursera course: Suppose we have a set of examples and Brian comes in and duplicates every example, then randomly reorders the examples. We now have twice as many examples, ...
2
votes
1answer
55 views

Predict observation using Hidden Markov Models

I have a sequence of observations e.g. ["Click","Scroll","Hover","Zoom","Select"]. I need to predict the next value of this observation sequence but not the next hidden state. I know that there are ...
0
votes
0answers
9 views

Represents istances with multiple values for an attribute and similarity between them

In the scenario in which I'm working each entity could be represented in terms of ten distinct properties that I will call p1, p2, ..., pn. For each of them, an entity, can have its specific range of ...
0
votes
0answers
25 views

Minimizing the Training data

I have a grey-box model of the form Y= a + b X1 + c X2. Where a, b and c are the coefficients based on regression. The regression variables X1 and X2 are determined based on ...
0
votes
0answers
5 views

Design a feature with time and presence information

Context: I am working on a decision tree classifier, trying to classify businesses as to whether they are likely to have an event occur (default) in the next 90 days. One input I get is whether, and ...
0
votes
1answer
30 views

Conceptual question on optimization

What is the intuition and the physical meaning of the mathematical expression in convex optimization? When using optimization algorithms like particle swarm or genetic algorithm, do they have ...
4
votes
1answer
527 views

What is being learnt?

This may seem a trivial question but I have a fundamental problem in understanding learning. In supervised learning, given and input-output pair, what are we learning? Are we learning the inputs ...