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

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Heterogeneous Domain Adaptation without training data from target domain

Are there any strategies to learn a model that can classify data from one domain using only data from a different domain for training? For example, suppose I have a bunch of data from two different ...
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8 views

How to create my own dataset as input to neural network with csv file data using R [on hold]

I am new to machine learning. I am interested to know how datasets are created and fed as input into neural network package in R. I have a CSV (comma separated values) file and some description pages ...
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5 views

How to back-propagate error in Lenet-5 CNN?

Recently I'm trying to implement the Lenet-5 CNN. But I stuck in how to propagate error from the conv-layer to previous layer, for example, from C3 layer to S2 layer. Could anybody please help me?
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7 views

Problem in coverting class of variables and reading exported file.csv [on hold]

I read a data set which is in .csv format in to R and I've made manipulation on the data and also changed some of the variables class from integer to factor. after, I exported the new data variables ...
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28 views

How to Normalize data

I'm new to predictive analytics. I have data variables which are highly skewed, I want to normalize those for better predictions. I've used normalization,standardization. but they gave same data ...
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1answer
17 views

What characteristics should the input data have for a neural network?

I am planning to use a neural network for prediction. For example, to predict whether a student will pass a course based on his previous academic records or characteristics. I was wondering how to ...
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15 views

detecting start and end of a sub sequence

I have a sequence that is 60 vectors long and I need to get as a output integer which are indexes of vectors that are start and end of sub sequvence. I have around 800k samples, but at the moment I'm ...
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1answer
28 views

Authors frequently mention the convergence of their reinforcement learning algorithms. Do they imply a local or a global convergence?

I frequently come across authors in reinforcement learning papers mentioning that some or the other algorithm converges. Do they mean a local convergence or a global convergence? What do they ...
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2answers
22 views

how many data should we choose for training and testing the neural network?

I am using MLP neural network. My question is for training the neural network and testing it how much splitting of data is needed like is there any rule that I always have to split data 70% for ...
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1answer
22 views

Course for Latent Dirichlet Allocation

Considering Andrew Ng played a major role in the development of LDA, I find it surprising that there is no video with him explaining it, similar to his machine learning course. Is there any good ...
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35 views

SVM different results in R with same input [migrated]

I have developed a SVM model for fraud detection in a train dataset using the following parameters: ...
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14 views

kernel size and stride value for fully convolutional network for semantic segmentation

I am not very clear about some technical details in implementing Fully Convolutional Networks for Semantic Segmentation. The paper discusses three models: fcn32, fcn16 and fcn18. According to this ...
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45 views

Why is my high degree polynomial regression model suddenly unfit for the data?

I'm building a ridge regression model in scikit-learn and trying to find the optimal degree polynomial to use. The data I'm working with is a fairly predictable time series of hourly traffic volumes, ...
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32 views

By use NaiveBayes, get result in probabilities (0.5, 0.7) not only in 0 and 1

I have tried to find an answer, but could not. If I use NaiveBayes on train data after find optimal settings apply it on test data. but the result which U receive is 1 or 0. By using DISCRETIZER I ...
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9 views

How to design RNN\VAE for sparse sequential data?

I am currently struggling with designing some Deep Learning algorithms that takes as an input sparse sequential data, since the system I try to model signals sparsely (once in a while). I try to ...
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34 views

Feature scaling of non-Gaussian data before SVM

I have data for a binary classification problem and was wondering generally what to do if the different dimensions/features of your input training data display vastly different distributions in ...
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21 views

When doing regression with a singled layered Neural Network, what activation function is the best one to choose?

I was training a singled layered (shallow) neural network as in: $$ f(x) = \sum^K_{k} c_k\theta(W_k x+b_k)$$ for regression (using squared error loss) or function approximation. I was wondering, is ...
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1answer
40 views

Multivariate time series clustering

I am collecting a group of multivariate time sequences. For example, there are 2000 time series. Each time series is of 12 dimensions. Are there any systematic models/algorithms that can cluster ...
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1answer
37 views

Why is “the true data generating process” similar to simulating the entire universe?

In the deep learning book by Bengio, Goofellow and Courville (http://www.deeplearningbook.org/) there is paragraph in the regularization chapter. "Deep learning algorithms are typically applied to ...
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25 views

Time Series Prediction using Machine Learning

I am trying to predict the request arrive time of some objects in network traffic. I have few features of the object like their type, size, previous arrival time, etc. So I was think that I should use ...
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20 views

What models are well suited to varying-length feature vectors?

I'm trying to classify a bit of a strange-looking data set where the input vectors have a variable length. The actual data is pretty boring, but imagine a related problem where you're trying to ...
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20 views

Sparsity as missing data + MLE

I just had this "funny" idea: what about a classifier that not only tries to learn weights for predicting y but actually works with "deleted" data (as in sparse ...
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1answer
30 views

What exactly is a background class in a classification problem?

I have heard people using the term background class in the following to scenarios: For a class which has a very high number of instances compared to other classes in a classification problem ...
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14 views

RMSD vs. Log-loss

I have four vectors of numbers, one is the ground truth (binary, rach number is either 1 or 0), the other three are the corresponding prediction (each number is a probability from 0 to 1) generated by ...
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1answer
18 views

How to get from input depth to output depth in convnets?

See this example: convnet quiz Udacity. How to get from input depth = 3 to output depth = 8? My assumption: In this example we have 8 filter (kernels) and each of them slides over the 3 inputs. So in ...
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6 views

Time interval prediction

I want to predict a specific time interval(ex. patient processing time in clnic) with some boolean value like whether this patient has cough or whether he/she has certain disease. I have tried using ...
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7 views

If number of examples is smaller than number of features, but greater than size of my filter, is my convolutional network still okay?

I'm training a 5-layer convolutional neural network on about 300 images with size $30 \times 20$. We can say that the number of features before the first layer is $600$. So it seems like I don't have ...
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1answer
18 views

Classification: training sets different sizes

I'm building a classifier for text analysis sentiment. I have a large training set for positive, neutral and negative mentions. Should the training data sets be similar in size? Currently my ...
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46 views

Time Series Data Mining and Correlation guidance [on hold]

Introduction Editing this post to clarify what I am asking. This is a bit of a vague question to begin with. I am not asking if this particular statistics method with these variables will cause this ...
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17 views

Data science : where to find Project Examples using Python with SQL? [on hold]

I’ve learnt Python and SQL, but was suggested to “work in Python to analyze data sets stored in MySQL”. I am following CS109 which uses Python, but it does not really use SQL https://www.quora.com/...
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57 views
+50

Help in problem formuation :Hebb's learning

In the supervised learning problem, the goal is, given a training set, to learn a function $h : X \mapsto Y$ so that $h (x)$ is a “good” predictor for the corresponding value of $y$. If $y$ takes ...
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1answer
12 views

Prediction of next event occurrence

I am working on a request prediction problem in which I have to predict which object will be requested in the near future and how many times. This is like a basic internet traffic request pattern on a ...
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1answer
33 views

Should I optimize neural networks that are part of ensemble of neural networks?

I'm creating ensemble of neural networks for a simple binary classification task. Every neural network is generated and trained a bit differently (number of hidden layers, number of neurons per layer, ...
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75 views

Prisma App Neural Networks [on hold]

Can anyone point me towards relevant material that would allow me to understand how Prisma works? http://prisma-ai.com/index.html
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33 views

Regression + Time series

I have time series data about sales/day, but i also want to include other data (static/dynamic) to forecast the time series. Is it possible to combine ARIMA model and regression models to achieve the ...
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19 views

Test Support Vector Machine on its own Training Data: issues and observations

I recently concerned myself with SVMs and Sparse Bayesian Machines (RVMs) and was performing feature scaling of my highly skewed data prior to the training of my algorithm. When I was subsequently ...
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34 views

Family of flexible parametric mappings $f_\theta:(0,1) \rightarrow \mathbb{R}$?

For the purpose of reparameterizing a model (mostly with the goal of improving MCMC efficiency), I am looking for a family of flexible parametric mappings $f_\theta:(0,1) \rightarrow \mathbb{R}$ such ...
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1answer
30 views

Bootstrapping test set?

Let's say I have a classification problem with a small and fixed test set. If I train a classifier and report the accuracy on this test set, I know that this estimate has a high variance. Does it make ...
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25 views

Training instances importance in Random Forest?

Is it possible to determine the importance of the training examples in Random Forests, analogously to what's done with predictors? Basically the idea would be to find important samples in the data, ...
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12 views

measure the inter-relationship among a set of multi-variate data points

Given a group of multivariate data points, are there any ways to formalize/quantify the relationship of one given point comparing to other data points in this group. Clustering maybe one approach. But ...
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16 views

How can I apply discriminant analysis (multivariate) if my data is not normally distributed?

As LDA and QDA assume normality and homocedasticity, and my data is not that well behaved... is there any other kind of robust technique I could use? Could applying box cox transformations and then ...
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10 views

How common is it to train each network independently then train end-to-end in a neural network?

Say that I had 3 autoencoders stacked on each other. How common is it to ...
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1answer
31 views

How does data augmentation reduce overfitting?

I'm trying to understant the benefit apported by the step of data augmentation in a classification algorithm. I have a vector of hexadecimal strings and a column vector containing the label ...
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1answer
21 views

What happens inbetween switching test and training sets in k-fold cross validation

The general idea of k-fold cross validation is to partition your test data from your training data, then within your training data you make another partition where all but one of those partitions are ...
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40 views

how to preprocess/feature stacle multimodal input data?

I am wondering how to normalize data for the use of SVMs etc. that has a clear non Gaussian, i.e. non unimodal distribution. I wrongfully scaled the data by subtracting the mean and dividing the std ...
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21 views

Interaction between two random variables [on hold]

I have a machine learning system called Sys. Sys uses a variables set (Var) applied to a ...
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1answer
32 views

What method for classification with 24 variables, 14 categories and 100,000 obs.?

I have a dataset with more than 100,000 observations (rows) and 24 variables in which 23 are continuous and one is categorical variable. The categorical variable has 13 categories (1, 2, 3, ..., 13) ...
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12 views

Using sentiment data to predict stock prices

I have implemented a model to predict "last" price of stock of 5 days ahead. I have used RandomForestRegressor. In addition to this, I have a data set of sentiments. It is the data from social media ...
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statistics metrics or machine learning methods to quantify the relationship between a point and a group

There are several data groups: A1 = { A11, A12, A13, …, A1n} A2 = {A21, A22, A23, …, A2n} .. … … Am = {Am1, Am2, Am3, …, Amn} Here ...
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11 views

Is this the right approach to sequence learning with a LSTM?

I am currently learning to use Keras and am trying my hands on NILM (Non-intrusive Appliance Load Monitoring). In NILM the goal is to disaggregate the whole house power data into single appliances. I ...