Questions about computer algorithms that automatically discover patterns in data and make good decisions based on them.

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Research work on computational models for a “specific” person's behaviors

Is there active research work on creating computational models of a "specific" person's behaviors (general behaviors, emotions, actions...)? What are some references for such research? I tried google ...
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152 views

Trying to implement a simple neural network, without success [on hold]

(Background: I am a Civil engineer, and decided to read through Mitchell's ML book) I am trying to replicate an example from Mitchell's book (first edition), Figure 4.7 (later editions seem to have ...
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1answer
22 views

Text features in decision tree

Right now I am doing some problems on application of decision tree/random forest. I am trying to fit a problem which has numbers as well as strings (such as country name) as features. Now the library, ...
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16 views

Maximum likelihood estimate for softmax function

Given an undirected graphical model with no edges and only N nodes, I am trying to find a closed form solution to the ML estimate of each node given that $p(x|\theta)=\frac{\exp(\sum_{s\in ...
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8 views

get rules of Weka J48 Classification tree [closed]

I want to display a Weka classification tree to the end-user that I have generated using weka.jar in Java. But not the default tree in grey window titled as "Tree Visualizer" which is obtained via ...
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9 views

When selecting most accurate classification model out of N models using cross validation, will our confidence in the answer decrease as N increases? [closed]

In our machine learning class our lecturer argued that when we have to select the best model (least error/most accurate) using cross validation out of N models our validation error will decrease but ...
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18 views

DAG that can capture any joint distribution

I am trying to do the following question: draw a directed acylic graphical model on five variables which can capture any joint distribution. I'm not sure I understand what it means by "can capture any ...
5
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1answer
62 views

PAC learning axis parallel rectangles

I am trying to understand the proof that the axis parallel rectangles are PAC learnable in the realizable case. This means that given $\epsilon, \delta$ with enough data we can find a function $h$ ...
3
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1answer
36 views

What does does $O$ mean in this context?

I understand big O notation in computational complexity theory, but I don't see how it applies in the equation below. From Pattern Recognition and Machine Learning: If we weren't familiar with ...
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1answer
41 views

Bayes net: algorithm to calculate joint distribution?

I recently started studying bayesian networks and I am now implementing an exact inference algorithm: enumeration. I am aware of the complexity and inefficiency of this method but I want to fully ...
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1answer
33 views

What is an edge hop?

I've tried googling it, but found nothing. Here is the context it's in: From Bayesian Reasoning and Machine Learning: Adjacency matrices may seem wasteful since many of the entries are zero. ...
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2answers
57 views

What happens when you don't use a metric in k-means?

K-means is a clustering algorithm which works like this: ...
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0answers
19 views

Hessian-Free instead of LSTM for Recurrent Net Machine Translation

Last year, Ilya Sutskever and collaborators came out with a paper about a recurrent LSTM net that learns sequence to sequence mappings for machine translation. It's somewhat surprising that the ...
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1answer
33 views

Build Automatic Speech Recognition (ASR) from scratch [closed]

I want to build a Automatic Speech Recognition (ASR) engine for myself, but I've no idea from where to start. I've read that most ASR's are build upon Hidden Markov Models, but also I've read that ...
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14 views

Amplifying a Locality Sensitive Hash

I'm trying to build a cosine locality sensitive hash so I can find candidate similar pairs of items without having to compare every possible pair. I have it basically working, but most of the pairs in ...
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1answer
33 views

Why is the most probable assignment for all variables in MRFs called MAP assignment?

I am new to graphical model, especially Markov Random Fields. I have a question about MAP assignment. Let say we have the graph structure and all the potential functions. MAP assignment in MRFs is ...
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81 views

Who coined the term “machine learning”?

I'm trying to figure out who coined the term "machine learning". An ancillary question is from where is Arthur Samuel cited as defining the field of "machine learning" in 1959 as: the field of ...
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0answers
31 views

where should I begin : I need to predict power consumption of a household?

I have the power consumption values of a household for 2 year. To preserve privacy I am down sampling this data before sending it to the utility company. My goal is to get back the original data from ...
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1answer
8 views

Adding concept drift to data sets

I'm about to work with concept drift problem in data streams. I need to start with real data sets from UCI machine learning repository and add to them concept drift (in attributes domain). Do you ...
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2answers
43 views

Learning Quadratic Functions

I have seen in some ML tutorial that functions of the form $f(\vec x) = \vec x^T A \vec x$ ($\vec x \in \mathbb{R}^n$ and $A$ is an $n\times n$ real matrix) can be PAC learned. Can anyone point me ...
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18 views

Ridge regression with more small errors

I've been using kernel ridge regression. My problem requires predicting some values which I know to be integers. By rounding the results to the nearest decimal, I get excellent results. However, If ...
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1answer
65 views

Cognitive Computing vs Artificial Intelligence?

Can anyone please tell me the difference between them? A brief definition of Cognitive Computing would appreciated. Also how does cognitive computing relate to neural networks? Thank you~
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0answers
32 views

How regression is used in item-based collaborative filtering?

In the paper "Item-Based Collaborative Filtering Recommendation Algorithms" In section 3.2.2 about regression, it is said the the user's actual rating of item N (Ru,n), is replaced with an estimate ...
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1answer
35 views

How does the ID3 Algorithm differ from a generic Decision Tree learning algorithm

Based on the notes of my Machine Learning lecturer, I am struggling to understand how the two algorithms differ? Both seem to select the most informative feature A (based on least entropy), then ...
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28 views

Is a KNN-Classifier memory intensive?

In my opinion it seems fairly obvious that a $k$ nearest neighbours (KNN) Classifier would be fairly expensive in terms of memory, as the model is the training set itself. However, any notes I've read ...
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23 views

Learning Hierachial representation of objects in a unsupervised manner

I am trying to understand how the how a hierarchal representation of an object can be learned in a unsupervised manner, using the method described in this paper. ...
3
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1answer
112 views

Genetic Algorithm, Neural Network, Deep Learning, Machine Learning Similarities and Applications? [closed]

I am a computer engineering student and trying to get the idea behind all these Artificial Intelligence Concepts and applications. I know little theoretically about machine learning and some high ...
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2answers
70 views

ANN - Backpropagation with multiple output neurons

Can I utilize the backpropagation algorithm in a layered, feed-forward ANN in instances where there are multiple output neurons? If so, how? Links to (somewhat) comprehensible resources would be ...
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1answer
33 views

what is the general name of this problem?

what is the general name of a problems where learning agent observe new data as a learning goes on. For example when playing platformer games one must incrementally learn new level areas and states ...
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38 views

Formula for number of parameters in an undirected graphical (probability) model

I have googled endlessly, and I cannot find it. Can anyone point me to a reference that gives a way to calculate the number of parameters in an undirected Graphical Model? Adapting from the similar ...
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11 views

how binary quantile regression divides the dependent variable into quantiles

I am not very clear with binary quantile regression. As if it was ordinary quantile regression, it would divide the dependent variable's value by its ascending value into quantiles. But I cannot ...
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0answers
63 views

Prerequisites for AI and machine learning [closed]

I am interested in the field of AI and machine learning and I am fairly good at mathematics and statistics and programming in general. However I lack a formal CS education and my undergraduate degree ...
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1answer
32 views

Random Forest closed loop

I'm using the Random Forest algorithm for classification. I have some variable to use as features in input, but I was wondering if I can use the output of the classification itself as input. Suppose, ...
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1answer
32 views

Feature values range

Suppose I am about to use SVM for learning a classification or ranking function. Suppose that my feature vectors are two dimensional and that values for one dimension are, say, natural numbers and the ...
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18 views

recommender system using SVD

I have followed the following steps listed in any introductory text,on how to do matrix factorisation using SVD on the Movielens dataset. replace all zero entries with mean rating of the movie(some ...
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47 views

Learn feature value range for a classification

Which domain does this problem belong to? Given a set of products some are classified as cheap and some not. The task is to determine the price range (probabilistic) for cheap products. Supervised ...
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37 views

Similarity based document classification compared to the user profile ratings

I need to understand a distinction to check if I understood a document classification problem right. Given a training set of documents with ratings, that a user has given, let's say rating $r_{k} \in ...
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17 views

How does SARSA handle episode termination

When applied to domains that are episodic and have a "final" state but no final action, like a game, how does SARSA update the Q-values? e.g. A game agent would receive this series: ...
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2answers
77 views

Why is the O(nW) algorithm for the Knapsack problem not a polynomial one?

On the wikipedia page for the knapsack problem it says that the runtime is $\mathcal{O} (nW)$ and goes on to say that this doesn't violate its classification as NP because the input size is related to ...
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28 views

Merging two disconnected graphs

Firstly, I'd like to apologize for any misused terms or ways I could have made the description much more succinct. It's been a while since I took machine learning during my bachelor's. I have two ...
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0answers
16 views

How to make logical inference from simulated data

I have data collected from a computer simulation of football games which seem to have recurring patterns of the following form. if madrid plays arsernal and the match ends under 3 goal, then on ...
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1answer
34 views

How to identify statistically distinct features of different sets?

I have two non-overlapping sets of items, with feature counts for each. What standard algorithms can I use to extract the most statistically distinct features of each set? For example: Items served ...
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1answer
32 views

What's the difference between Adaptive Control and Hierarchical Reinforcement Learning?

After watching Travis DeWolf presentation on scaling neural computation, I'm a bit confused about the difference between Reinforcement Learning (whether hierarchical or not) and Adaptive Control. They ...
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29 views

What's the difference between adaptive control and a kalman filter?

From my basic understanding of Adaptive Control, I understand that it uses the error and the velocity of the error to approximate the error in the solution space of a problem, thus allowing for ...
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0answers
27 views

Training a model to match two time series

Context I have two related time series, I want to learn to produce one from the other. However, they aren't synchronous, and the lag between the two does not revert to the mean, it accumulates. ...
2
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1answer
52 views

I am having trouble understanding (and implementing) logistic regression for classifying into three classes

(For reference, i am using Kevin P Murphy's Book "Machine Learning: A Probabilistic Perspective" and implementing with MATLAN - without any toolboxes) I have a dataset with 392 samples (rows), each ...
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24 views

backpropagation algorithm seems to be forcing output values to middle than extremes

I have been playing around with artificial neural networks lately, specifically with the prospect of trying to replace the contrastive divergence algorithm with some type of evolutionary metaheuristic ...
3
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1answer
301 views

How does the momentum term for backpropagation algorithm work?

When updating the weights of a neural network using the backpropagation algorithm with a momentum term, should the learning rate be applied to the momentum term as well? Most of the information I ...
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16 views

Is this an accepted/valid clustering evaluation metric?

We have a clustering algorithm where the number of clusters isn't known to the algorithm - it iteratively creates clusters out of similar-looking data points. The evaluation metric we're currently ...
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1answer
81 views

How exactly do you calculate the hidden layer gradients in the backpropagation algorithm?

I have been going through the description of the backpropagation algorithm found here. and I am having a bit of trouble getting my head around some of the linear algebra. Say I have a final output ...