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

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Basics in learning machine learning by building/doing - (in python) [on hold]

I am totally new to "machine learning" and am looking for how to get started. Can you point me to a few resources, geared for the beginner, that are excellent starting points? What are the main ...
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1answer
180 views

Feature selection + classification in Caret

I'm using Caret to apply a bunch of different machine learning algorithms for phenotype prediction from gene expression data. With about 20,000 genes, I'd like to perform filter feature selection ...
10
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1answer
74 views

Computation of the marginal likelihood from MCMC samples

This is a recurring question (see this post, this post and this post), but I have a different spin. Suppose I have a bunch of samples from a generic MCMC sampler. For each sample $\theta$, I know the ...
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1answer
154 views

UCI Machine Learning Data Set: Parkinson Speech Dataset with Multiple Types of Sound Recordings Data Set

I would like to use the data set Parkinson Speech Dataset with Multiple Types of Sound Recordings Data Set from UCI to test pattern recognition algorithms. However when I plot the features and ...
0
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11 views

Neural networks that can reach state-of-the-art accuracy with two or three hours training?

Are there some neural networks that can reach state-of-the-art accuracy with two or three hours training, on dataset like CIFAR, MNIST,etc...
3
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1answer
49 views

Is it reasonable to study neural networks without mathematical education?

Given the modern state of machine learning technologies and tools (e.g. TensorFlow, Theano, etc.), it seems like entry threshold have recently lowered and it is enough to be able to program on, say, ...
3
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2answers
1k views

Using neural networks for multi target prediction

I have a spatial dataset with some xs and ys at different spatial locations. I want to learn a non linear regression function using neural networks. I looked in to the training data and the outputs ...
0
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1answer
12 views

Applying filters learned from convolutional neural networks

I have a neural network that I trained on 32 * 32 px size images. Can I use these filters learned from the network on larger images not used in training the network such as a 600 * 800 px image? Or ...
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9 views

Comparing and evaluating win probabilities in sports from different settings

Background I'm trying to predict the probability that the home teams wins a certain sports game, for each minute of the game. Taking these win probabilities together produces a nice visual of the ...
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2 views

finding and comparing temporal and location-specific pass patterns in a soccer game

I have data on several soccer matches, where ball passes have been recorded in terms of XY-location where the pass starts, XY location where the pass ends, the team making the pass, the player making ...
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1answer
13 views

Convolutional Neural Network for 3D point cloud?

Can Convolutional Neural Networks or Deep Architectures be used for generating 3D point clouds ?
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1answer
29 views

true negative is 0% whereas true positive is 100% correctly classified

I used Naive Bayes from Spark's MlLib to train a model and test it on the data (in the form of an RDD). The results were confusing. the data and results are as follows: The problem is a binary ...
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2answers
308 views
+150

Are all models useless? Is any exact model possible — or useful?

This question has been festering in my mind for over a month. The February 2015 issue of Amstat News contains an article by Berkeley Professor Mark van der Laan that scolds people for using inexact ...
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12 views

choosing prior parameters for variational mixture of Gaussians

I am implementing a vanilla variational mixture of multivariate Gaussians, as per Chapter 10 of Pattern Recognition and Machine Learning (Bishop, 2007). The Bayesian approach requires to specify ...
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0answers
4 views

how to choose parameters for paralysis of neural network classificator?

I have multilayer feedforward neural network and I learn it to do multi classification using back propagation method. Can you provide an example of how to create paralysis of neural network (when nn ...
4
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0answers
20 views

Recurrent Neural Network (RNN) topology: why always fully-connected?

I've started reading about Recurrent Neural Networks (RNNs) and Long Short Term Memory (LSTM) ...(...oh, not enough rep points here to list references...) One thing I don't get: It always seems that ...
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18 views

Scaling overlapping subsets, optimizing nearness [on hold]

For a set of two thousand xyz points, I am multiply-scaling the z values. The points exist in overlapping subsets. Each subset is scaled by a float variable. In all there are anywhere from ...
8
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5answers
7k views

In Naive Bayes, why bother with Laplacian smoothing when we have unknown words in the test set?

I was reading over Naive Bayes Classification today. I read, under the heading of Parameter Estimation with add 1 smoothing: "Let $c$ refer to a class (such as Positive or Negative), and let $w$ ...
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1answer
54 views

Predicting lat/long from binary features

I have a number of observations that occur around my city (a small area), and several of them have latitude and longitude. I have been looking into predicting the latitude/longitude of the ...
3
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1answer
147 views

In word2vec, for analogies do we use “in” or “out” vectors?

In word2vec each word is associated with two vectors (one for in and one for out) so that it predicts conditional probability: $$P(word_{out}|word_{in}) = \frac{\exp(v_{in} \cdot ...
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2answers
5k views

Collinear variables in Multiclass LDA training

I'm training a Multi-class LDA classifier with 8 classes of data. While performing training, I get a warning of: "Variables are collinear" I'm getting a training accuracy of over 90%. I'm using ...
4
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4answers
127 views

How to compare features and classifiers which achieve perfect accuracy?

So I'm looking to compare different combinations of features and classifiers. But I'm getting a lot of combinations that achieve 100% cross validation accuracy. I'm trying to figure out how I would ...
0
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1answer
26 views

Does memory ever really matter for mini-batch size selection?

I'm new to machine learning, and am confused about some aspects of stochastic gradient decent. I've read in several places that, when using vectorized code, the reason that mini-batching in ...
2
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0answers
20 views

Predict revenue of click

I'm trying to build a model for eCommerce that will predict revenue of a click that comes via online-marketing channels (e.g. google shopping). Click goes directly to product detail page (so it's not ...
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1answer
117 views

What is the purpose of the scaling factor used in dropout?

I have a question related to the dropout function in the LSTM tutorial: http://deeplearning.net/tutorial/code/lstm.py ...
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10 views

Adversarial sequential learning with a linear model

I have a problem with the following characteristics: The value of an observation is a function of its predictors The nature of the relationship between value and predictors changes slowly over time ...
3
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0answers
27 views

Defining Groups of 1D Data In a Time Series

I'm trying to analyze the following data. I have a large collection of time collections, and for each time collection, I want to figure out groups of times that are between longer groups. Imagine ...
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13 views

Simple Bayesian Classifier for spam detection

I am a very beginner at machine learning, and I'm reading a book about it. I came across some lines of code in R for naive bayesian classification for spam detection. This is the code: ...
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1answer
26 views

Mixture Density Network: What is C?

I'm currently trying to implement a Mixture Density Network (MDN) based off of the original paper here. Most of the equations seem pretty straight forward but on page 6 (7 of the PDF) equation 23 has ...
1
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1answer
138 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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18 views

tensorflow: How to feed numpy.ndarray? [on hold]

I hope this is the right community to ask in. I decoded a JPEG image and have it in the shape n_samples x n_features as a two-dimensional numpy.ndarray. I feed this to tensorflow as following: ...
2
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2answers
1k views

Popular named entity resolution software

I am working on a project and need to extract persons' names from a large amount of documents. This task should belong to the named entity resolution problem. What are currently some of the most ...
1
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1answer
26 views

Why are gradient boosting regression trees good candidates for ranking problems?

I have been reading up on gradient boosting machines, and in particular GBRT's. I've come across numerous mentions (and finally tracked down some papers) on applying these models to ranking problems - ...
26
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5answers
13k views

Neural networks vs support vector machines: are the second definitely superior?

Many authors of papers I read affirm SVMs is superior technique to face their regression/classification problem, aware that they couldn't get similar results through NNs. Often the comparison states ...
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5 views

svm audio classification [on hold]

I am working in project for classifing a human voice with SVM and it is based on the MFCC coefficient. I have the program in matlab to calculate MFCC, it gives 12 vector of MFCC. and I have the ...
0
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1answer
174 views

using the SVM for sound classification

I am new here, and I have found only this and this to be useful. However, I still have some queries to make after doing the following things: Extraction of the features from the Audio files. Scaled ...
0
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0answers
16 views

How is the 'tau' variable calculated in locally weighted linear regression? [on hold]

I was wondering how the tau variable is calculated in locally weighted linear regression. Is it determined by humans, or is there an algorithm to determine its ...
0
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0answers
13 views

Variational Autoencoder for feature extraction

I would like to ask if would it be possible (rather if it can make any sense) to use a variational autoencoder Auto-Encoding Variational Bayes for feature extraction. I ask because for the encoding ...
3
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4answers
3k views

Methods & CRAN packages to predict probability using neural networks or others machine learning algorithms

I have a medical database containing 7 input variables (4 are binary) and a binary outcome variable (Survival: yes/no). My objective is to train and test an algorithm that predict probability of ...
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0answers
9 views

How to classify group of events (or actions) from an event log

Given data that contains events carried by group members (e.g., in the format of 'group_id, member_id, event_id, timestamp'), and label for such history data mapping a sequence of group interaction ...
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29 views

Repeated training examples in Gradient Descent

I am new to machine learning and trying to understand stochastic gradient descent. I understand in stochastic gradient descent, in each epoch, randomly an example is picked and given to the model. So ...
0
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1answer
28 views

Random Forest model good train and test performance but bad “real world” performance

I am working on a classification problem where I need to classify objects based on a visual data. There are a couple hundred different classifications to be made and I have around a million plus ...
0
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2answers
142 views

Classification with confidence scores: is regression ok?

Say you have a binary classification problem, but you'd like to have a sense of how confident the classifier is by using a numerical score and then using a threshold for the binarization. This can be ...
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-1
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14 views

handwritten hindi numeral recognition using SVM

I have used LIBSVM to classify the hindi handwritten numerals . I have got a result of 93.38 % , when I used C=500 and Gamma = .004. I would like to get an idea about k fold cross validation and the ...
0
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1answer
40 views

What model to use for predicting future expenses of an individual?

I am currently working on a Personal Finance application, which tracks expenses of a person. When entering an expense entry, the user selects the category of the transaction (e.g. 'Bills', 'Food', ...
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6 views

converting feature from string to categorical reduces classification accuracy

I am working on San Francisco crime classification problem from kaggle. https://www.kaggle.com/c/sf-crime during the work I encountered something unexpected. I applied scikit learn's random forest ...
3
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1answer
44 views

Bayesian treatment of outliers

In a supervised learning problem, I have a training dataset $D$ comprised of samples $x$ and their corresponding labels $\omega$. From this data, I attempt to learn the true distributions ...
0
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1answer
7 views

Handling missing/rare levels in predictor in data samples

Let us assume we have a dataset with one catigorical variable, which is represented in R as a factor. I am performing crossvalidation to assess models, for which I need to perform stratified sampling ...
2
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1answer
181 views

Boosting: why is the learning rate called a regularization parameter?

The learning rate parameter ($\nu \in [0,1]$) in Gradient Boosting shrinks the contribution of each new base model -typically a shallow tree- that is added in the series. It was shown to dramatically ...