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

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Derivation of derivatives in backprop stage of convolutional neural network

I'm reading the online book "Deep Learning" by Ian Goodfellow et al. . In section 9.5 Variants of the basic convolution function: Directly quoting from the book, "Suppose we want to train a ...
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2answers
207 views
+50

How to prove this Gaussian Mixture inequality? (Fitting/Overfitting)

Let f[x] be a Gaussian Mixture pdf with n terms of uniform weight, means $\{\mu_{1},...,\mu_{n}\}$, and corresponding variances $\{\sigma_{1},...,\sigma_{n}\} $: $$f(x)\equiv\frac{1}{n}\sum_{i=1}^{n}\...
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1answer
8 views

Interpreting multinomial logistic regression in scikit-learn

I am running a multinomial logistic regression for a classification problem involving 6 classes and four features. Here is the code: ...
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0answers
6 views

Should using SMO classification in WEKA take so long with large dataset?

I have a dataset of 205 features and 238000 samples. It is a combined dataset of several subjects' data that I want to use for between-subject classification. I am using WEKA 3.8 with a 64-bit JVM ...
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8 views

Support vector data description (SVDD) [on hold]

How can i explain the dual of the SVDD method?. what's the influence of linear term on the dual objective function?
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1answer
23 views

relationship between fully connected layer and convolutional layer

When reading about the transforming the fully connected layer into convolutional layer, posted in http://cs231n.github.io/convolutional-networks/#convert. I just feel confused about how to understand ...
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1answer
17 views

Bottleneck building block in Residual learning networks

I am wondering about how 1x1 convolution can be used to change the dimensionality of feature maps in a residual learning network. Here the top 1x1 convolution changes the feature map size from 256 ...
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1answer
127 views

What is “fitted function” in the context of boosted regression tree?

I'm following the tutorial of package dismo's boosted regression tree, which produces two graphs, about fitted function and <...
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12 views

Logistic Regression - Confuse equality

Title : Logistic Regression - Confuse equality Problem : Prove that : $\Delta E(in) = -\frac{1}{N} \sum_{n=1}^N \frac{y_n x_n}{1 + e^(y_n w^t x_n)}$ = $\frac{1}{N} * \sum_{n=1}^N - y_n x_n \...
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1answer
79 views

Is there a model for machine learning on non-aggregated data, where we have a target variable, but also a grouping variable

The background first: I am currently working on some predictive modelling of some client shopping data to see if it is possible to categorise clients into one of nine ordinal categories according to ...
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1answer
12 views

Why are the weights of RNN/LSTM networks shared across time?

I've recently become interested in LSTMs and I was surprised to learn that the weights are shared across time. I know that if you share the weights across time, then your input time sequences can ...
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1answer
23 views

Are there disadvantages using proportional features instead of absolute values?

I was wondering whether there are disadvantages in using proportional features instead of features with absolute values. For example: I have the following data set, which includes, TV duration, ...
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10 views

Single pass object detection

Let we have a set of images $\{\mathcal I_i\}_{i=1}^n$ with labels $\{\mathcal B_i\}_{i=1}^n$, where each $\mathcal B_i$ is a set of regions. The problem is to find a function that given image $\...
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2answers
17 views

Neural network input values belonging to classes

I need help on configuring a neural network. I would like to pass in accelerometer values (x,y,z) from two different sensors, and have the network compute the corresponding angle. I am providing close ...
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0answers
7 views

Calculate EER from FAR and FRR?

I'm wondering if we have FAR and FRR scores for each threshold if we can compute an EER programatically? Say we have: ...
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1answer
88 views

How can I experiment with Lagrange multiplier in PCA optimization?

Suppose we want to solve following optimization problem (it is a PCA problem in this post) $$ \underset{\mathbf w}{\text{maximize}}~~ \mathbf w^\top \mathbf{Cw} \\ \text{s.t.}~~~~~~ \mathbf w^\top \...
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1answer
169 views

Machine learning tutorials / examples on data sets larger than a terabyte

I am trying to gather a list of practical ML examples / tutorials on more than a terabyte of data. I'm particularly interested in feature extraction from large data sets that involves aggregation (the ...
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31 views

Creating training data for machine learning

I am creating a machine learning model but I don't have any data as such that can be used as training data. All I now that there are certain independent features/columns(categorical and numeric both) ...
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42 views
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How does one interpret histograms given by TensorFlow in TensorBoard?

I recently was running and learning tensor flow and got a few histograms that I did not know how to interpret. Usually I think of the height of the bars as the frequency (or relative frequency/counts)....
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11 views

knn text clasiffication model error when term is not found in new documents

Using KNN model for topic clasification. My model uses 200 variables (terms) and 10 target labels, using R (tm package). Accuracy is fairly good. Now, new documents are arriving that need to be ...
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0answers
17 views

How to improve the sensitivity of minority class on imbalanced datasets

I am working on a classifier which stratifies a population of samples into different classes. The class distribution (ground truth) is imbalanced, and the prevalence of each class is: $$\begin{...
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1answer
394 views

Chi Squared Kernel and Faster implementation

There is a good implementation of Chi-Squared Kernel in http://www.vlfeat.org/matlab/vl_alldist2.html But this implementation is very slow when input data is huge. This implementation doesn't accept ...
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1answer
16 views

can ML learn concepts in an unsupervised manner

I am wondering if ML can currently learn concepts in an unsupervised manner and how would that work. For example, when it looks at a transaction, I would like ML to understand the concept of the ...
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4answers
6k views

What does interaction depth mean in GBM?

I had a question on the interaction depth parameter in gbm in R. This may be a noob question, for which I apologize, but how does the parameter, which I believe denotes the number of terminal nodes in ...
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1answer
21 views

How to calculate average EER and ROC?

If I test a system on 3 users and I obtain FARs and FRRs for thresholds and an EER for each user, how do I obtain an average EER and plot a ROC? As I see it, I could average the 3 EERs I have ...
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1answer
28 views

Understanding the role of the discount factor in reinforcement learning

I'm teaching myself about reinforcement learning, and trying to understand the concept of discounted reward. So the reward is necessary to tell the system which state-action pairs are good, and which ...
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1answer
23 views

regarding the convolutional network structure of FCNN

The paper of Fully Convolutional Networks for Semantic Segmentation , gives the following image, . What do those numbers represent, 96, 256, 384, etc? Are them ...
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16 views

Cost functions like cross-entropy in backpropagation for non sigmoid activation?

I was following this resource. Cross-entropy function was introduced as cost function. When calculating gradient in backpropagation we get delta values which depend on derivation of activation ...
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1answer
33 views

varying classification threshold to produce ROC curves.

How can I vary classification threshold to produce ROC curves. I am new to R and I wanted to classify in different algorithm. Since the accuracy of ROC plot is high I wanted to change the threshold ...
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1answer
788 views

How to perform hypothesis testing for comparing different classifiers

I am trying to classify a small dataset (around 500 records) into two classes. I used various methods like SVM, Naive Bayes and k-nn classifier. Now, I would like to set the results from one of the ...
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15 views

Machine Learning: Non-Linear Regression over dataset with very similar predictors and very different targets

I have a time-series dataset collected by a group of biologists counting the abundance of a particular animal species in an area. I later enriched this dataset with weather variables (e.g. temperature,...
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18 views

Machine Learning: How to solve “class imbalance” in Regression Algorithms?

I have a time-series dataset collected by a group of biologists counting the abundance of a particular animal species in an area. I later enriched this dataset with weather variables (e.g. temperature,...
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1answer
41 views

How to deal with hierarchal / nested data in machine learning

I'll explain my problem with an example. Suppose you want to predict the income of an individual given some attributes: {Age, Gender, Country, Region, City}. You have a training dataset like so <...
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82 views

Recommended Data Augmentation Techniques for Deep Convolutional Neural Networks

Introduction&Background: My experience with deep learning research has shown me that data augmentation is one of the most important techniques one can use to improve performance (unfortunately?)....
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1answer
32 views

What is a poselet?

I've seen the term "poselet" mentioned a few times (e.g. A and B) as some sort of construct used in facial recognition.
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52 views

Calculating conditional probability in Bernoulli mixture model

I'm following the book Pattern recognition and machine learning by Bishop on Bernoulli mixture model, and trying to code it. But I don't understand how to calculate the conditional probability (page ...
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0answers
22 views

Observation dependency in logistic regression to learn relevance of search results

I want to predict the most relevant item from a set of search results resulting from a query. Moreover, these items are places; the query is at a lat/long and time; and the search results are ...
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The proof of lasso regression solution? In which it shrinks some of coefficents to zero? [duplicate]

I would like to know how the lasso method shrinks some of coefficients exactly to zero, "for example could show me how that works mathematically if there are two parameter". For example, with ridge ...
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1answer
34 views

Distribution of the sum of the two dependent bivariate gaussian distributions and related questions

This is something I was thinking about and I decided to modify a question from a mid-term to ask this. Suppose $X_{1}$ and $X_{2}$ are two bivariate gaussian variables, decribed as $$ X_{i}=\begin{...
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4answers
931 views

Evaluating a regression model

For classification problems I have been using Neural Networks and measuring Type I and II error using the confusion matrix and its measures as per this resource which is pretty straight forward. ...
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1answer
15 views

regarding computing output size for convolutional layer

I am following up the lecture notes posted on http://cs231n.github.io/convolutional-networks/ I am sort of confusing about one example given in the notes. It says ...
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0answers
45 views

Restricted Boltzmann Machine : how is it used in machine learning?

Background: Yes, Restricted Boltzmann Machine (RBM) CAN be used to initiate the weights of a neural network. Also it CAN be used in a "layer-by-layer" way to build a deep belief network (that is, to ...
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1answer
16 views

How do I know if a model with a subset of the features of another model has lowest training/test error?

I'm doing the Machine Learning specialization from the University of Washington on Coursera, and I have to answer some questions in a quiz from the Regression course. They ask which model would have ...
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2answers
125 views

Simple kNN example

Can someone explain, in very simple way, how can kNN algorithms predict classes of set of points? Is there any resource for beginners to understand algorithms with graph?
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41 views

Is it possible to use Machine learning for price elasticity [on hold]

Predict sales on everyday basis and flow of sales in the order window. For example, on 3-1-2016, the predicted value comes out to be 10000, so we need to predict the flow in the order window that sums ...
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0answers
6 views

The derivative of the absolute value |x| [migrated]

I read about the derivative of the absolute value |x|, but why the absolute value is not differentiable at point zero, and when it becomes 1 or -1 {geometrically}? Thanks
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1answer
51 views

Measuring Accuracy of the SVM based model

I have developed a model which evaluates a user based on how important he is for the organization. For that purpose I have generated 1000 records for 1000 users. Here I have one dependent variable "...
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15 views

timeseries forecasting when datapoints doesn't start at same time period

I have a dataset which has lifecycle information of different products but all the products doesn't start selling in same period or same year/quarter.In this case how should i do time series modelling ...
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2answers
16 views

Correlation based feature selection(CFS) tool

Is there any tool or script that was implemented for correlation based feature selection? My feature vector data is in a large-scaled data file, so if I use tools like Weka for feature selection, I ...