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

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Use Edge detection in Image classification

I am having five types of objects (flower, building, face, pair of shoes and a car) in my object recognition and i need to classify these. Identifying through edges in this type of data set seems to ...
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4answers
973 views

Why is Logistic Regression called a Machine Learning algorithm?

If I understood correctly, in a Machine Learning algorithm, the model has to learn from its experience, i.e when the model gives the wrong prediction for the new cases, it must adapt to the new ...
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1answer
44 views

What does “node size” refer to in the Random Forest?

I do not understand exactly what is meant by node size. I know what a decision node is, but not what node size is.
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1answer
48 views

Anomaly detection in time series data

Hi I have a large data set of objects, each containing a list of the same attributes. The data is arranged in a time series so that the value for an attribute for an object is indexed by its time. I ...
3
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1answer
16 views

nested cross-validation

if my outer cv is 5-fold, after the process, i have 5 final models, then apply these 5 final models from each CV to the whole dataset (training+validation+testing). For my case, the final 5 accuracy ...
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1answer
28 views

Using Fisher LDA in R

I have run a large study looking at traumatic brain injury in patients I have conducted CT scans on patients very soon after the injury as well as neurocognitive testing and then repeated this at 1 ...
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1answer
33 views

Can I Interpret the impact of variables like positive or negative on the model by Random Forest, as I can do by Logistic Regression

I have created a model for prediction of candidates presence or not . I have used Logistic Regression and Random Forest . By Logistic Regression, I got coefficients associated with 100 features and I ...
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1answer
28 views

Independence of data points assumption

While reading an ML book, I realized that most of the time, the input data points are correlated with each other, and hence their observation is not independent. But then, why do we assume that the ...
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0answers
13 views

R | NA/NaN/Inf in foreign function call | e1071 SVM [migrated]

Dataset: https://archive.ics.uci.edu/ml/datasets/Chess+%28King-Rook+vs.+King-Pawn%29 Code: ...
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0answers
20 views

Conclusion from PCA of dataset

I have a set of data for sequence labeling. I did PCA with (with 2 principal components on the x and y axis) on the dataset and it turns out as below: Using an LSTM network to classify the dataset ...
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0answers
39 views

How are radial basis functions (RBFs) networks extended to use multiple layers?

I am trying to understand the interpretation of radial basis functions (RBFs) as networks and then trying to understand the relationship it has to "normal" neural networks and how to extend them to ...
3
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2answers
116 views

Can a neural network learn a functional, and its functional derivative?

I understand that neural networks (NNs) can be considered universal approximators to both functions and their derivatives, under certain assumptions (on both the network and the function to ...
2
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1answer
47 views

What is the right algorithm to detect segmentations of a line chart?

To be concrete, given 2D numerical data as is shown as line plots below. There are peaks on a background average movement (with small vibrations). We want to find the values of pairs (x1, x2) if those ...
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0answers
35 views

How is prior knowledge of letter/word patterns incorporated into handwriting (or speech) recognition?

Using handwriting recognition as an example, we can train various models to recognise individual characters but to actually be useful we must incorporate prior knowledge of common character sequences, ...
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0answers
55 views

what is 1/0 in this article?

I am reading the article with title "metric learning by collapsing classes" lately http://papers.nips.cc/paper/2947-metric-learning-by-collapsing-classes.pdf . Everything goes well until the equation ...
2
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1answer
36 views

How does one do Stochastic Gradient Descent (SGD) on an objective function that has a regularizer?

I know that for Stochastic Gradient Descent, one picks a data point $(x_n, y_n)$ at random from the training set $S_N$ and then updates the parameter of the model in question. If the cost function ...
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0answers
7 views

Bigdata cluster compatible distributed predictive model [migrated]

I might be asking a dumb question but my question is can I write a python program (lets say a classifier) using some library that scales in hadoop (not only using a simple parallel processing).The ...
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1answer
76 views

What algorithm can I use to find correlations between events?

I am new to machine learning so I am trying to find some literature but I'm not even sure what to Google for. My data is of the following form: ...
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1answer
28 views

sampling distribution of the mean for arbitrary 1-D pdf

I want to compute the sampling distribution of the mean for $k$ samples from an arbitrary, known probability density $f(x)$, with $x \in \mathbb{R}$. What is the most efficient way to do so ...
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1answer
27 views

what is the relation between “data visualization” and “embedding”? [closed]

I am reading several articles about metric learning lately. Sentences like "build better data visualizations via embedding" and "low-dimensional linear embedding of labeled data" pop up very oftenly. ...
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0answers
20 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 ...
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0answers
53 views

Why do we have to be concerned about the problem of overfitting on the training set?

For a hypothesis set $H=\{h_1,...,h_M\}$, randomly sampled training set $D_{train}$, and a learned hypothesis $g$ using $D_{train}$, the VC-bound of a finite hypothesis set tells us $$ ...
3
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1answer
44 views

How do gradients propagate in an unrolled recurrent neural network?

I'm trying to understand how rnn's can be used to predict sequences by working through a simple example. Here is my simple network, consisting of one input, one hidden neuron, and one output: The ...
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2answers
66 views

Is it right to consider the output of the neural network as its confidence in predicting the output?

Suppose I have a single output sigmoid (tanh) that is producing an output ranging [-1, +1]. Is it right to consider this output as its confidence measue of predicting the output. The output value ...
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1answer
34 views

Real time example: Estimation for incomplete data

Following is from Csiszar and Shields' FnT monograph "Information Theory and Statistics": The expectation–maximization or EM algorithm is an iterative method frequently used in statistics to ...
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1answer
28 views

How can I do a one vs all classification (binary classifier) with a neural network

I have a set of images that belong to a particular class. Then, I have another set of images that do not contain any image of the above particular class. So, effectively I have two sets of images ...
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1answer
37 views

Predicting Co-Ordinate Data

This is on a prediction model we were trying out among a bunch of us trying out ML for the first time. Basically I have a training data set of network user ID's with their location (latitude and ...
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1answer
40 views

Can one derive Radial Basis Functions (RBFs) with movable centers from Tikhonov regularization?

It is well know that the "usual" Radial Basis Function can be derived from Regularization that imposes small derivates. More precisely it is well known that the following: $$ f(x) = \sum^{N}_{n=1} ...
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0answers
10 views

Choosing keypoints for a training set and their prospective number

I am building a software to classify cells from images taken by a microscope. I have a dataset of images of cells to use as training dataset - I have extracted Keypoints from each image using ORB - ...
3
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1answer
121 views

Neural network Equation question

I am looking at an example for the activation function $a_1$. Why does the equation look like $\Theta_{10}x_0 + \Theta_{11}x_1 + \Theta_{12}x_2 + \Theta_{13}x_3$ instead of $\Theta_{10}x_0 + ...
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In statistical learning theory, isn't there a problem of overfitting on a test set?

Let's consider the problem about classifying the MNIST dataset. According to Yann LeCun's MNIST Webpage, 'Ciresan et al.' got 0.23% error rate on MNIST test set using Convolutional Neural Network. ...
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0answers
13 views

Normalizing features extracted from image for training a model

I am trying to build a software to classify cells from images taken by a microscope: First, i have a dataset of images of cells to use as training dataset - I have normalized the images and extracted ...
2
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1answer
54 views

What balancing method can I apply to a imbalanced data set?

I'm trying to solve one classification problem from the UCI database repository. Unfortunately (or fortunately), I've noticed that my dataset is imbalanced. I've structured the data as 5 classes, ...
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what possile balancing method can I apply on a imbalanced data set? [duplicate]

Im trying to solve one classification problem from de UCI database repository. https://archive.ics.uci.edu/ml/datasets/Student+Performance Unfortunately (or fortunately) I've noticed that my dataset ...
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66 views

Feature scaling and mean normalization

I'm taking Andrew Ng's machine learning course and was unable to get the answer to this question correct after several attempts. Kindly help solve this, though I've passed through the level. ...
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1answer
57 views

In learning theory, why can't we bound like $P[|E_{in}(g)-E_{out}(g)|>\epsilon] \leq 2e^{-2\epsilon^{2}N}$?($g$ is our learned hypothesis)

Given Data $D_{in}$, number of data $N=|D_{in}|$, and hypothesis set $H=\{h_1,h_2, ...,h_M\}$. For a fixed hypothesis $h$, for example $h_1$, we can derive ...
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0answers
35 views

Why RMSE is too small?

Sorry, I'm a newbie at recommender systems, but I wrote few lines of code using the Apache Mahout library. My dataset is pretty small, 500x100 with 8102 cells known. The dataset is actually a ...
2
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1answer
54 views

Scikit Binomial Deviance Loss Function

This is scikit GradientBoosting's binomial deviance loss function, ...
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0answers
27 views

For a low-rank regularized PCA, what is the limit of dimension reduction for a given p and n of data?

Here p is the dimension of data, and n is the number of data rows, so the data matrix is a $n∗p$, and if we use PCA for dimension reduction, and in this case it is a low-rank regularized PCA, what ...
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1answer
22 views

Incorporating new words in tfidf feature-vector for online clustering

I am building an Online news clustering system using Lucene and Mahout libraries in java. I intend to use vector space model and tfidf weights for Kmeans(or fuzzy/streamKmeans). My plan is : Cluster ...
2
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1answer
23 views

Choosing a sample rate for GBM models

I've created several GBM models to tune the parameters (trees, shrinkage and depth) to my data and the model performs well on the out-of-time sample. The data is credit card transactions (running into ...
3
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1answer
46 views

Including Interaction Terms in Random Forest

Suppose we have a response Y and predictors X1,....,Xn. If we were to try to fit Y via a linear model of X1,....,Xn, and it just so happened that the true relationship between Y and X1,...,Xn wasn't ...
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0answers
19 views

Nested Cross validation vs Ordinary CV

Usually nested cross validation procedure is used when the tuning parameters of the model are estimated simultaneously to the model assessment. According to the theory, the ordinary CV is not suitable ...
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1answer
31 views

Prediction for a large number of discrete numbers other than classification and regression

I am dealing with a problem where the output of my model, can have numbers like 1-3000 (around) (score in a game). This is like a score in a game. Giving a least squared error setting, for a model, ...
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2answers
57 views

How to explain KNN in Bayesian probability?

I am wondering how to explain k-nearest neighborhood algorithm from a Bayesian approach, especially on how to justify the best choice of k value?
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0answers
41 views

A small help in understanding basic SVM

I am on the course of learning SVM. So, I am having a doubt. Suppose in the case of 2D, a point needs to be classified. So, let say I am having a point x(2,3). So according to the equation wx+b >= 1 ...
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0answers
22 views

Uplift model with a continuous outcome?

Does anyone know any good packages (preferably in R/python) or references that are specifically about building the uplift model with a "continuous" outcome? I've used the upliftRF from R and made it ...
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1answer
45 views

How to setup for really deep convolution model

I'm rather new to deep learning and convolution network and got some basic models to run. However, when I tried to build a deep CNN model (i.e., more than 14, 15 layers) the error rate does not seem ...
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1answer
61 views

Ranking based on binomial data (example: website conversions)

Let's say I have a database of all websites and their corresponding viewers and buyers. I want to rank the websites based on the conversion rate or where we see more conversion. So example: ...
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0answers
6 views

Special Type of Classification without focus on accuracy of one type

Hi I am running into this problem here: I have some features say a, b , c and a binary type indicator X= 0 or 1 (good or bad). Similar as decision tree algorithm, what I wanted is to get ...