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

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Maximum Likelihood Estimate (MLE) equivalent to finding $\hat y$ in linear regression with i.i.d. Gaussian noise distribution

In an assignment I need to show that for linear regression, with the noise i.i.d. Gaussian distributed $\epsilon_i \sim N(0,\sigma^2)$, that finding the Maximum Likelihood Estimate (MLE) is equivalent ...
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24 views

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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8 views

Classification on variable-length time series

I have a series of transactions like the following: [0, 2, 2, 3, 1, 0, 0, 0, 1] [1, 0, 0] [3, 3, 1, 1] I would like to classify each transaction as being part of one of two categories: class A or ...
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19 views

regularization in Machine learning [on hold]

Within the context of regression, we use regularization as a way to “control” the complexity of the hypothesis class; hence regularization introduces our own bias into the learning problem. True or ...
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1answer
15 views

Valid result when adding two kernels with negative coefficient?

If $k_1$ and $k_2$ be a kernel in $ \mathbb{R}^n \times \mathbb{R}^n $. we know $k(x,z)=ak_1(x,z) + bk_2(x,z)$ (kernel addition) is still a valid kernel if $\: a,b \geq 0\,$ ($a,b$ is real numbers, ...
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17 views

Cross-Validation gives different result on the same data

I have done Cross-Validation by crossval function in matlab on my data, but when I run the Cross-Validation many times, it give me a different results, so is that ...
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1answer
79 views

Should you tune 'ntree' in the Random Forest algorithm?

In the original paper, I was under the impression that the RF couldn't really overfit. However, in practice I'm seeing that increasing 'ntree' sometimes increases test set error. Is this due to ...
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15 views

Clustering on this reinforcement learning approach?

I am trying to create an agent that selects an action depending on a state that gives back maximum reward. To keep things simple I will keep it to two actions and 24 different states. The states is ...
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31 views

two margin comparison and one conclusion?

I read following notes, and couldn't get it. any idea or hint would highly appreciated. a SVM classifier using a second order polynomial kernel. The first polynomial kernel maps each input data x to ...
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1answer
21 views

variance in test accuracy will increase as we increase the number of test examples؟

I see this statement on 1 that say a True statement on Machine Learning Context. The variance in test accuracy will increase as we increase the number of test examples. my challenge is why ...
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13 views

1) State 2) Action and then 3) Reward diagram: Which ML approach to use?

It is looks like a reinforcement learning diagram however it's slightly different. I'll explain the numbers. 1) The environment first gives the agent a state 2) The agent does it's magic and then ...
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27 views

Supervised learning, unsupervised learning and reinforcement learning: Workflow basics

Supervised learning 1) A human builds a classifier based on input and output data 2) That classifier is trained with a training set of data 3) That classifier is tested with a test set of data 4) ...
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16 views

Which machine learning(recurrent/reinforcement learning) method/algorthim would suite this scenario

This application has it's roots in public transport, users opening the application and looking at the departure times of buses for specific stops (page 1) or planning a journey from location A to B ...
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115 views
+100

calculation threshold for minimum risk classifier?

Suppose Two Class $C_1$ and $C_2$ has an attribute $x$ and has distribution $ \cal{N} (0, 0.5)$ and $ \cal{N} (1, 0.5)$. if we have equal prior $P(C_1)=P(C_2)=0.5$ for following cost matrix: $L= ...
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15 views

linear discriminative analysis for regression

LDA computes a projection matrix to maximize class conditional probability. Similar to this, is there any exisiting method or library for jointly learning latent space and minimizing the regression ...
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8 views

EM to Variational EM in LDA

Why exactly, when learning hidden variables distribution in LDA(Latent Dirichlet Allocation), one cannot use to the EM (Expectation Maximization) algorithm and have to resort to a variationnal EM ...
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1answer
75 views

some inference about k-NN algorithms for better understanding? [on hold]

I ran into some facts make me confusing. for k-NN classifier: I) why classification accuracy is not better with large values of k. II) the decision boundary is not smoother with smaller ...
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1answer
13 views

What is the difference between linear perceptron regression and LS linear regression?

Recently, a project I'm involved in made use of a linear perceptron for multiple (21 predictor) regression. It used stochastic GD. How is this different from OLS linear regression?
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18 views

Are there shape-matching scores that take into account multiple scales simultaneously?

Say you are determining how well your model of an object matches an image. To score this match, we can use e.g., the cross-correlation coefficient (CCC), giving an overall shape match. This works for ...
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14 views

Matching a query distribution to a family of template distributions

I was turning over a hypothetical question in my head: Suppose I have a set of template probability distributions, let's say each giving the probability of the occurrence of certain objects like ...
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92 views
+50

How we can calculate Fisher criterion weights?

I studying for Pattern Recognition and ML. I ran to one type of question: We define equal prior probability as: $P(D_1)=P(D_2)= \frac{1}{2}$ in two-class classification problem, if the ...
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82 views

How many features will be selected by mutual information and wrapper in information filtering?

I see one example in old-mid exam from well-known person Tom Mitchell, as follows: Consider learning a classifier in a situation with 1000 features total. 50 ...
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21 views

What is the commonly used mehtod for measuring variance of accuracy mean using k-fold cross validation?

I know there are planty of questions about standard deviation, though I didn't find an answer tuned to my particular need and also I could really use your help! I'm performing 18-Fold Cross ...
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1answer
37 views

a challenge with linear classification and distance to origin? [on hold]

I ran into a problem, when studying on linear classification. my prof. says: in a linear classification $y=w_0+w_1x_1+w_2x_2$ that depicted on following figure, distance of origin to decision ...
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9 views

How to create and format an image dataset from scratch for machine learning?

I've only worked with ML with .csv formats. I've worked with image formats too but only premade imagesets (MNIST,etc). If I were to create an imageset from scratch, how are the class labels typically ...
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12 views

graph classification task - multi label?

I have a data set in graph format representing semantic connection between terms. The data set is divided into clusters, each with several labels (not unique, or mutually exclusive, no set number of ...
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9 views

Learning from streams with concept drift in real world - how to handle missing class problem?

In currently delves into learning from streams with concept drift. As more I learn I think about how I can use learning algorithms on real data. Most of drift detection algoritms to evaluate is ...
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174 views
+50

Compute the probability that the provided classifier label is correct

A binary SVM classifier provides a label $y_c^{(i)}$ for each $i$-th sample provided. This is not assured to be corresponding to its true label $y^{(i)}$, since the classifier could have computed a ...
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14 views

How to visualise the uncertainty of the classification?

I used SVM to do some classification, and SVM can output some probabilities (likelihood) value measuring how likely each data to be one particular class. For example, Data point 1: 90% (class 1) 5% ...
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2answers
32 views

Semi-supervised Learning Training

I have got some data partially labelled. Therefore, I would like to apply semi-supervised learning for this dataset. Basically, I trained the Support Vector Machine (SVM) using the data with labels ...
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21 views

Learning if instances from a dataset are part of the same subset

I was wondering if there are some well-known machine learning methodologies for subset learning. In other words, to learn if two instances are part of the same subset or not (boolean label?). One ...
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2answers
20 views

Prediction of features given predictor

I am working on a problem where my objective is to predict y given some features x1,x2,x3,...x8,x9 I solved this problem using some statistical and machine learning techniques like regression, trees, ...
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1answer
55 views

Relation between variance of eigenvalues and the effectiveness of PCA on the data

If the covariance matrix has eigenvalues $$\lambda_1 \ge \lambda_2 \ldots \ge \lambda_d > 0,$$ why is the variance of the eigenvalues, $$\sigma^2=\frac{1}{d}\sum_{i=1}^d (\lambda_i-\bar ...
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32 views

ParagraphVector od doc2vec for classification tasks [closed]

I am really interested to use doc2vec on classification task. I'dont understand how they create the featurevector for classification. I am trying to sum all words and paragraph id of the sentence to ...
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29 views

Support Vector Machine image classification in R

I'm looking for some direction for creating/running a support vector machine (SVM) classification on a multi-band Landsat image in R. What I have: Landsat 8 image with 8 bands plus a NDVI, and ...
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4answers
867 views

Introduction to machine learning for mathematicians

In some sense this is a crosspost of mine from math.stackexchange, and I have the feeling that this site might provide a broad audience. I am looking for a mathematical introduction to machine ...
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24 views

Feature selection for logistic regression [closed]

Not sure if the feature selection is the correct term but assuming I have data x,y | z where x and y features and z is target. And the task is to classify z using x,y but I know that data is not ...
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17 views

How to merge different predictive models training with different data sets?

Is there any good method to merge/consolidation different predictive models which were trained on different features but outputs the same goal. Example: Model 1 with features a + b + c (trained on ...
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1answer
41 views

Buiding Ensemble model

I'm new to ensemble model. Suppose I've KNN models like this - (in R) library(class) ...
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9 views

Importance of the norm of the weight vector in the perceptron algorithm

I have exhausted all possible searches online on the role of the norm of the weight vector in binary classification. The only information i am getting is that it prevents over-fitting. I don't see how ...
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10 views

How to include only true positives and false positives, that is ignore false negative classifications in a confusion matrix?

I have performed a 10 fold cross validation on my data set using binary decision trees. I've got 6 trees (to detect one of the six basic human emotions from facial data points) trained for each fold. ...
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19 views

Find repetitive patterns in matrices below

How can I identify the repetitive patterns from the matrices below? My problem is that the patterns in the matrix are different from matrix to matrix (dependent on the input data). I need some machine ...
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16 views

Apply trained MDS model to new data

I have both a distance matrix and the original vectors, and am using MDS (Multidimensional Scaling) with R to generate vectors in more dimensions for the data. With dimensionality reduction (for ...
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1answer
31 views

How to train a model when instead of a target we have a range where it is?

Often in machine learning we have a situation when target is numeric (real or integer). Each target comes with an associated input vector. The goal is to learn the mapping from the input vectors to ...
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1answer
53 views

Manifold learning: does an embedding function need to be well behaving?

I am trying to learn about manifold learning techniques; a family of methods in machine learning. According to this idea, there is a low ($d$) dimensional, hidden space where the real data generation ...
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26 views

Incorporating metadata to a supervised Topic Model

I have texts and their metadata and a response variable (how many times the text has been read). I'm interesting in finding out how the latent topics in the set of texts are related to the popularity ...
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1answer
59 views

What is “Prediction Accuracy (AUC)”, and how is it the number conducted in Machine Learning?

Here is the link in question: http://applymagicsauce.com/documentation.html When the Cambridge University Psychometric Center's "Apply Magic Sauce" defines how their ...
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21 views

machine learning to extend a matched sample (sanity check)

I'm a bit new to machine learning, but I want to try to use it in a project I'm working on. Specifically, I'd like to use it to identify a sample of potentially rare events. I'm not entirely sure if ...
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33 views

How could the predictive mean in a GP become negative when both the prior and the training target values are non-negative?

I am training a Gaussian process regression where the training target values are between 0 and 1 and the prior mean is the fixed zero function. The predictive mean sometimes becomes negative e.g. ...
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63 views

Which library is the easiest to start with for Deep Learning

I am quite proficient with Machine Learning libraries and now want to get into Deep Learning. I am even quite comfortable with neural networks as far as understanding back propagation algorithm is ...