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

learn more… | top users | synonyms (1)

0
votes
0answers
9 views
0
votes
0answers
19 views

How is 'memory' implemented in Neural Networks?

I looked around into various articles on NN. I cant seems to grasp a basic idea - how a NN would remember what it has learnt? For example lets say there is a NN which was trained to recognize a ...
0
votes
0answers
12 views

Neural Networks. General approach to predict nearest future value (recognise incomplete pattern)

I need a general idea (and learn a bit of terminology as well) on how to approach the following problem: I have data coming in real-time but in uniform intervals (1s). each portion can have 1 or ...
0
votes
0answers
8 views

Step training Baum-welch HMM

Referred to Baum–Welch algorithm, http://cs.au.dk/~cstorm/courses/MLiB_f14/slides/hidden-markov-models-4.pdf Is this formular correct ? I spend a couple days to figure out which part is wrong. I'm ...
3
votes
1answer
26 views

Spatial coordinates (latitude and longitude) are non significant

I want to use latitude and longitude as a feature for models like SVM or Logistic Regression (both for classification). What is the most common approach to use latitude and longitude values as ...
1
vote
0answers
38 views

Cross-Validation: how to choose k for small datasets (n=900)?

In a binary classification task, I have a small training set (n=900, 9 features). The two groups are not symmetric (1 = 560, 0 = 340). I also have a test set (n=400) where I don't know the class ...
0
votes
0answers
4 views

How to consider SSE as small or large value when you are ruuning K-Means large data set?

I am running K-means developed in java on 1M records with 15 variables then how to quantify minimum size of SSE, Always I am getting 5 digit no and with one open source software also I am getting the ...
0
votes
0answers
7 views

How to choose right step size for alpha in the Elastic net using glment package?

I'm using glmnet to learn different Elastic net regression.as you know, Elastic net would perform at least as good as Lasso regression. but it's not the case for me and Lasso perform better than ...
1
vote
1answer
34 views

Result of K-Means Algorithm Not Desired

I am learning about K-means algorithm, and I have generated a dataset with 150000 data points, with 10000 points per cluster. (Scatter plot at the bottom) When I run K-means on the dataset, I first ...
0
votes
2answers
36 views

'Punishment Function' in Number of Knots in Splines?

I was considering using natural cubic splines for my prediction problem when I had a thought: In Ridge Regression, you set out to minimize the equation; \begin{equation} F(X)=\lambda\sum_i ( b^2)+ ...
0
votes
1answer
27 views

How to bootstrap panel data?

I'm fitting some machine learning algorithms (e.g. SVM) on my panel data. It's taking too long for my entire dataset, so I'm considering generating smaller samples from bootstrapping then fit the SVM ...
0
votes
0answers
10 views

R: “weights” option will help calibrating class inequality?

I have a database with a binary response variable and 100 predictors (correlated and uncorrelated). I want to try the machine learning techniques in R I've been reading about in the last 3 weeks ...
0
votes
0answers
16 views

Deriving the maximum likelihood for a generative classification model for K classes

In Christopher Bishop's book "Pattern Recognition and Machine learning", there is the following question: Consider a generative classification model for $K$ classes defined by the prior class ...
0
votes
1answer
26 views

What is Nadaraya-Watson Kernel Regression Estimator for Multivariate Response?

Given a regression setting with covariates $X_{n \times m}$ and response $Y_{n \times p}$ where $p>1$, i.e the responses are vector-valued or multivariate, is there a Nadaraya-Watson estimator for ...
3
votes
1answer
60 views

Conditional independence iff joint factorizes

I have proven that: $X⊥Y|Z\ {\rm iff}\ p(x,y|z)=p(x|z)p(y|z)$ for all $x,y,z$ such that $p(z)>0$. The next question is to prove an alternative definition: $X⊥Y|Z$ iff there exist functions $g$ ...
0
votes
1answer
19 views

How to find optimal penaltyparameter C for SVM (regression)

I am training an svm regressor using python sklearn.svm.SVR From the example given on the sklearn website, the above line of code defines my svm. ...
0
votes
0answers
15 views

Machine Learning Predictors Evaluation Using R

I've bee using R for predicitve analytics and here is issue: I'm trying to predict the species (categorical variables E1, E2, E3 and E4) of an animal using as predictors a set of nominal (NO1, NO2, ...
0
votes
0answers
11 views

Python “feature_importances” for most important factors

I'm a little unsure as whether this belongs in stackoverflow or cross validated. I have found a few posts on this topic , but I have not found the following question. Is it accurate to run the feature ...
0
votes
1answer
30 views

SVM parameters clarification

James et al. in An introduction to the statistical learning (p. 351) claim that the solution to the support vector classifier problem involves only the inner products of the observations. They ...
0
votes
1answer
32 views

After Clustering, how can I evaluate which features had the biggest impact?

I've just performed unsupervised clustering (using DBSCAN) on a dataset for which I have no expert knowledge on. I'm interested in working out which features had the greatest impact on my clustering. ...
1
vote
0answers
20 views

How do I find corresponding clusters in independent samples?

Lets suppose you believe that observations in your data come from K natural but not directly observable categories and you wish to identify these categories with minimal prior assumptions, so you find ...
0
votes
1answer
33 views

Machine learning algorithm to predict next user's destination

I'm searching for a way to formulate my problem as a machine learning problem. Suppose I have a history of user's locations, and I want to predict his next location, similar to how Google Now does it ...
0
votes
1answer
10 views

Why do two identical feature vectors (distance score 0) get different labels in DBSCAN?

I have two identical feature vectors. They have a distance score of 0. I perform DBSCAN Clustering (using sci-kit) and they get different labels. Is this expected behaviour?
2
votes
2answers
69 views

Backward feature selection with CV model selection

I am thinking about doing the following to a data set with $N$ samples and $m$ features 1) Train using semi-supervised learning and cross validate on labeled data using LOO-CV to select the best ...
2
votes
0answers
37 views

Is There A Machine Learning Algorithm For Textual Data With Thousands Of Classifiers?

I've been asked to migrate this from StackOverflow to CrossValidated. I have a problem that I think Machine Learning can solve but am having a very hard time determining which ML Algorithm to use and ...
0
votes
0answers
29 views

Chi square and zscore - chose which one?

I'm newbie in machine learning. Recently I tried to learn something on this and got following concern: I have products classed by categories. Also I have users with gender and device model ...
0
votes
0answers
32 views

Advice for feature selection or feature extraction with semi-supervised learning

I am trying to solve a semi-supervised learning problem using LaplacianSVM. However, before applying LapSVM I would like either to perform feature selection or feature extraction. Furthermore, after ...
0
votes
0answers
27 views

Handling Sparse Data Frames - algorithm selection

I am new to machine learning/statistical modelling. I am trying to run a classification on a highly sparse dataset with 100 features, most of which are categorical (TRUE/FALSE) with the remaining ...
0
votes
2answers
49 views

k-mean clustering of week-times

I have data of meeting times. The data has weekday and hour of the day. I want to cluster the meeting times (I have reason to believe there are two different kinds of meetings that tend to occur at ...
1
vote
0answers
12 views

Data At Varying Granularity

I'm sorry for asking such a simple question, but for some reason it is throwing me off. By "granularity" I mean level of the data. For example, say in the classic example of spam classification you ...
1
vote
0answers
31 views

why do offline learning algorithms perform better than their online learning counterparts?

(I'm assuming infinite data, finite time for this comparison) I was wondering why it is exactly that online learning algorithms usually perform more poorly than their offline counter-parts.. Does ...
0
votes
0answers
16 views

Analysis of Feature Importances when features are dependent on one another

I can use random forests to determine which features are important when doing a prediction problem; for example. < height, weight, IQ measure> -> Is considered obese? Applying random forests ...
1
vote
2answers
49 views

Multi-class Classification using SVM with PCA

I'm doing an image classification task and the number of features of each example image is pretty huge (3,072: # pixels in each image). I'm thinking of using PCA to reduce the # features of each image ...
1
vote
0answers
32 views

How to estimate the correlated individual components from a sum, for a random process?

Assume that there are $N$ realisations of five individual, random variables$X_1$, $X_2$, $X_3$, $X_4$ and $X_5$, which in general could be correlated. We define another random variable ...
5
votes
1answer
36 views

Understanding sample complexity in the context of uniform convergence

I was reading Andrew Ng's notes and on page 6 he mentions (uniform convergence): $$Pr[\forall h \in \mathcal{H}_{finite}|\epsilon(h_i)-\hat{\epsilon}(h_j)| \leq \gamma] \geq 1-2ke^{-2\gamma^2m}$$ ...
0
votes
0answers
21 views

In Hidden Markov Model (HMM), is the transition matrix known, inferred, or assumed?

I'm reading Kevin Murphy's Probabilistic Machine Learning, which explains the forward algorithm to do filtering in HMM as follows (pp 610): The very first line says that the transition matrices ...
2
votes
1answer
24 views

Number of variables for decision trees

I have a data with just 5 independent variables and a response. I am dealing with a classification problem. Will decision trees perform well or the number of variables have to be higher to get ...
1
vote
1answer
39 views

Combining pca and classification algorithms

For some classification algorithms, assuming independence of data helps reduce the number of parameters to estimate. Why then not just to apply a method like pca or ica to the original features to get ...
0
votes
1answer
28 views

Machine learning : learn feature value range for a classification

Which domain the problem belongs to? Given a set of products some are classified as cheap and some not. The task is to determine the price range (probablistic) for cheap products ? Supervised ...
0
votes
1answer
37 views

Machine Learning for Text Classification

I am new to Machine Learning.I am working on a project where the machine learning concept need to be applied. Problem Statement: I have large number(say 3000)key words.These need to be classified ...
0
votes
0answers
33 views

Unsupervised learning algorithems to detect anomaly in waves

I have a sample of graphs (more then 10000...). that look like in the image below: I am searching an Unsupervised learning algorithems thet can help me to detect Anomaly observations. Here what i ...
0
votes
0answers
22 views

How can one go about recognizing a kind of motion using 3D depth data?

I'm using a Kinect device, and I'm currently extracting Joints, and Depth data unto probably a buffer data of 15 frames. This is done at 30 frames per second. The whole point of it is to try and ...
0
votes
0answers
29 views

Making a neural network model more sensitive to one of its several inputs

I am currently using neural network methods in R to model energy consumption (response) based on temperatures, previous consumption values and weekend dummy variables (inputs). Unfortunately, the ...
0
votes
0answers
27 views

SVM Dual Formulation :: KKT Constraint

In Andew Ng's SVM course notes, the final hard margin optimization problem is given as the following: I am unclear how to see from this where the 5th constraint is satisfied. The definition of ...
0
votes
0answers
11 views

Support vector regression in weka

I am using SVR for statistical down-scaling of precipitation. I have taken the first 3 factor scores in principal component analysis of variables as predictors and precipitation as predictand. As ...
0
votes
1answer
46 views

In natural language processing (NLP), how do you make an efficient dimension reduction?

In NLP, it's always the case that the dimension of the features are very huge. For example, for one project at hand, the dimension of features is almost 20 thousands (p = 20,000), and each feature is ...
0
votes
0answers
29 views

Gradients of marginal likelihood of Gaussian Process with squared exponential covariance, for learning hyper-parameters

The derivation of gradient of the marginal likelihood is given in this pdf, equation 5.9. But the gradient for the most commonly used covariance function, squared exponential covariance, is not ...
2
votes
1answer
74 views

How to estimate the individual components from a sum for a random process?

We have $N$ realisations of five individual, IID random variables $X_1$, $X_2$, $X_3$, $X_4$ and $X_5$. We define another random variable $S = X_1+X_2+X_3+X_4+X_5$. Now, for a given $S$ generated from ...
2
votes
0answers
30 views

Connections between d' (d-prime) and AUC (Area Under the ROC Curve); underlying assumptions

In machine learning we may use the area under the ROC curve (often abbreviated AUC, or AUROC) to summarise how well a system can discriminate between two categories. In signal detection theory often ...
3
votes
2answers
48 views

Mining patterns in continuous sequence

I have data in form of $N$ sequences $s_j=(t_i, e_i)_{i\in\{1,\ldots,n_j\}}$ with $n_j$ data-points each, where $t_i$ is a time-stamp and $e_i$ is a categorial event, say $e_i\in\{A,B,C,D\}$. The $N$ ...