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Questions tagged [machine-learning]

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

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12 votes
2 answers
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Smallest DFA that accepts given strings and rejects other given strings

Given two sets $A,B$ of strings over alphabet $\Sigma$, can we compute the smallest deterministic finite-state automaton (DFA) $M$ such that $A \subseteq L(M)$ and $L(M) \subseteq \Sigma^*\setminus B$?...
D.W.'s user avatar
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3 votes
1 answer
127 views

Analysis and classification based on data points

I'm not sure if this is the correct stack exchange or correct tags, but my question is as follows: I am working on a sort-of ratings system for players in a particular game. After allowing the ...
ctlaltdefeat's user avatar
103 votes
7 answers
23k views

Why is deep learning hyped despite bad VC dimension?

The Vapnik–Chervonenkis (VC)-dimension formula for neural networks ranges from $O(E)$ to $O(E^2)$, with $O(E^2V^2)$ in the worst case, where $E$ is the number of edges and $V$ is the number of nodes. ...
yters's user avatar
  • 1,417
46 votes
5 answers
14k views

Why has research on genetic algorithms slowed?

While discussing some intro level topics today, including the use of genetic algorithms; I was told that research has really slowed in this field. The reason given was that most people are focusing on ...
FossilizedCarlos's user avatar
29 votes
1 answer
29k views

Which machine learning algorithms can be used for time series forecasts?

Currently I am playing around with time series forecasts (specifically for Forex). I have seen some scientific papers about echo state networks which are applied to Forex forecast. Are there other ...
Maecky's user avatar
  • 393
29 votes
4 answers
5k views

How to determine likely connections in a social network?

I am curious in determining an approach to tackling a "suggested friends" algorithm. Facebook has a feature in which it will recommended individuals to you which it thinks you may be acquainted with. ...
phwd's user avatar
  • 621
19 votes
3 answers
10k views

Line separates two sets of points

If there is a way to identify if two sets of points can be separated by a line? We have two sets of points $A$ and $B$ if there is a line that separates $A$ and $B$ such that all points of $A$ and ...
com's user avatar
  • 3,179
8 votes
1 answer
5k views

Normalized measure from dynamic time warping

I am trying to find the similarity between two time series, but not in terms of distance, in something more sensible such as percentage of similarity. In other words I need something that shows the ...
aghd's user avatar
  • 183
4 votes
4 answers
1k views

Efficiently labelling training data in machine learning

Obviously it always depends on the specific case. However, my question is how to label data efficiently without writing a source code from scratch which solves the final problem itself? For example, ...
Outcast's user avatar
  • 151
3 votes
2 answers
944 views

Predicting energy consumption of households

I have the dataset which you can find here, containing many different characteristics of different houses, including their types of heating, or the number of adults and children living in the house. ...
user avatar
1 vote
1 answer
290 views

What are good counter-examples when training an apple classifier? [closed]

I am doing a project in order to recognize an apple. (I am using Emgucv with Visual Studio 2010 C#, if that's relevant). My project is a classification (is or is not an apple). I have 2000 images of ...
Emily Salazar's user avatar
36 votes
2 answers
3k views

Are there improvements on Dana Angluin's algorithm for learning regular sets

In her 1987 seminal paper Dana Angluin presents a polynomial time algorithm for learning a DFA from membership queries and theory queries (counterexamples to a proposed DFA). She shows that if you are ...
Artem Kaznatcheev's user avatar
30 votes
4 answers
71k views

What exactly is the difference between supervised and unsupervised learning?

I am trying to understand clustering methods. What I I think I understood: In supervised learning, the categories/labels data is assigned to are known before computation. So, the labels, classes or ...
Prot's user avatar
  • 403
30 votes
12 answers
10k views

Why is overfitting bad?

I've studied this lots, and they say overfitting the actions in machine learning is bad, yet our neurons do become very strong and find the best actions/senses that we go by or avoid, plus can be de-...
Friendly Person 44's user avatar
13 votes
5 answers
10k views

Machine Learning vs System Identification?

Could anyone explain to me the differences & similarities between machine learning and system identifications? Are these just two names of the same thing? In this page, they say: Machine ...
CherryQu's user avatar
  • 231
12 votes
1 answer
396 views

Google DeepDream Elaborated

I've seen a few questions on this site about Deep Dream, however none of them seem to actually speak as to what DeepDream is doing, specifically. As far as I've gathered, they seem to have changed the ...
Bob's user avatar
  • 121
10 votes
6 answers
6k views

Evolving artificial neural networks for solving NP problems

I've recently read a really interesting blog entry from Google Research Blog talking about neural network. Basically they use this neural networks for solving various problems like image recognition. ...
nmomn's user avatar
  • 377
10 votes
3 answers
942 views

Resources for studying the mathematical foundations of machine learning, for someone from a math/physics background

I am a soon-to-be physics graduate student with a background in theoretical and experimental cosmology. In my work, I've often found myself applying machine learning models and techniques for the ...
10GeV's user avatar
  • 201
10 votes
2 answers
4k views

Should activation function be monotonic in neural networks?

A lot of activation functions in neural networks (sigmoid, tanh, softmax) are monotonic, continuous and differentiable (except of may be a couple of points, where derivative does not exist). I ...
Salvador Dali's user avatar
9 votes
1 answer
9k views

What is the difference between 'features' and 'descriptors' in computer vision / machine learning?

I've read multiple time sentences similar to Finally, for standard image classification bag-of-words features based on SIFT descriptors have been found critical for high performances. We first ...
Martin Thoma's user avatar
  • 2,360
8 votes
1 answer
12k views

Machine Learning: Identify Patterns in Time-Series Data

I work in renewable energy. My company gathers a lot of data from equipment. This typically includes process data (such as transformer temperature, line voltages, currents, etc.) and discrete alarms (...
theoneandonly2's user avatar
7 votes
2 answers
10k views

Are neural networks a type of reinforcement learning or are they different?

Can neural networks be considered a form of reinforcement learning or is there some essential difference between the two? By the same token could we consider neural networks a sub-class of genetic ...
Tyler Durden's user avatar
6 votes
1 answer
8k views

Showing that Bayes classifier is optimal

Consider domain $X$, label set $ Y=\{0,1\}$ and the zero-one loss. Given any probability distribution D over $ X\times \{0,1\} $, we've defined the Bayes classifier $ f_D $ to be- $$ f_{D}(x)= \...
Alex Goft's user avatar
  • 215
6 votes
4 answers
9k views

Algorithms for Connect 4?

Im designing a program to play Connect 6, a variation of connect 4. I have narrowed down my options to the following: 1) Minimax with Alpha-Beta Proning 2) A Neural Net 3) Machine Learning My ...
user avatar
6 votes
2 answers
5k views

Does there exist a data compression algorithm that uses a large dataset distributed with the encoder/decoder?

If my goal were to compress say 10,000 images and I could include a dictionary or some sort of common database that the compressed data for each image would reference, could I use a large dictionary ...
Jason Livesay's user avatar
6 votes
2 answers
288 views

How does machine learning relate to artificial intelligence?

For example, is it a subset? Are they two separate fields in Computer Science? I have hear conflicting information: one professor said they are synonyms. However, in the courses taught at Stanford ...
rebecca kan's user avatar
6 votes
2 answers
471 views

What is usually the next step after showing the VC dimension?

I am new to statistical learning. I have a structure $X$ where I showed its hypothesis class $H$ has VC dimension $d$. All I know now is that I can bound the number of examples by $m\geq \frac{1}{\...
seteropere's user avatar
5 votes
4 answers
5k 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 ...
user avatar
5 votes
1 answer
187 views

SVM optimization objective: why are we maximizing $\frac{1}{\|w\|}$?

I'm learning about Support Vector Machines(SVM). I understood that their objective is to maximize the margin between the decision line and the closest point to it. For simplicity, let's assume we have ...
iensen's user avatar
  • 153
5 votes
1 answer
4k views

Are expert systems outdated, what are better alternatives to them, if any?

I need to link facts to actions through rules. If a person bought soup 10 times and he is coming at midday every day, then the system should link the fact that the person bought soup so many times, ...
Xeos's user avatar
  • 153
5 votes
1 answer
281 views

Google Deep Dream has these understandings?

From both my own exploration of Google Deep Dream using Dreamify for IOS, and from Google Image results on the topic. I've come to 3 conclusions about the networks understanding of images that seem ...
alan2here's user avatar
  • 209
4 votes
3 answers
334 views

How are Neural Networks made so general?

After reading this blog about Deep Neural Networks learning about selfies I'm struck by how generic the network in question is. In short: I'm thinking of trying to write something vaguely similar for ...
AncientSwordRage's user avatar
4 votes
2 answers
1k views

Is it a problem that "successful" machine learning algorithms have large VC dimension?

In my limited exposure, it appears that "successful" machine learning algorithms tend to have very large VC dimension. For example, XGBoost is famous for being used to win the Higgs Boson Kaggle ...
yters's user avatar
  • 1,417
4 votes
1 answer
384 views

Anomaly/outlier detection using fuzzy clustering

I understand that fuzzy clustering using FCM produces a membership matrix for the set of data points we feed to it. What characteristics will an anomalous cluster produced during this method have? (...
DaTaBomB's user avatar
  • 151
3 votes
1 answer
539 views

How much does regularization prevent overfitting?

I've been curious why machine learning algorithms with high VC dimension, such as XGBoost and deep learning, do well in practice. The answer appears to be regularization significantly restricting the ...
yters's user avatar
  • 1,417
3 votes
1 answer
141 views

Finding the dichotomy that maximizes information gain for a classifier?

Suppose that $\Omega$ is a finite probability space,with measure $P$ (we can take $P$ uniform). Let $C \in \{\pm 1 \}$ be a random variable on $\Omega$, the classifier. Let $$H(C) = H(C, \Omega, P) = ...
Elle Najt's user avatar
  • 374
3 votes
1 answer
3k views

PAC learning of axis-aligned rectangles

I've been reading the proof that axis-aligned rectangles are PAC learnable from the book Foundations of Machine Learning by Mohri (Proof pt. 1, Proof pt. 2), and a small technical detail stuck out to ...
Deroche's user avatar
  • 43
3 votes
1 answer
173 views

How did this work apply weakest precondition rule on their example car problem?

While reading the example given in [1]., I couldn't understand how the authors set up the logic to compute the weakest preconditions (wp) in their car example in section 4.2. The dynamics of the ...
desert_ranger's user avatar
3 votes
0 answers
2k views

Which machine learning algorithm is appropriate for predicting a vector?

I have a very large set of animal migration data, consisting of many series of vectors - each series is basically a path of a single animal. The dataset I'm using consists of 244 of these series. I ...
jeshaitan's user avatar
  • 173
3 votes
2 answers
153 views

Machine Learning: What program will derive the underlying algorithm in this series?

This is a machine learning question. Given this series of categorical data, what program will derive the underlying algorithm and predict what comes next in the series? Here is the series: B, BA, BB,...
Calvin's user avatar
  • 131
3 votes
3 answers
2k views

Support Vector Machines as Neural Nets?

This is more of a conceptual question. I have learned about Neural Nets, and I have some clue as to how Support Vector Machines work. I read somewhere however that given the appropriate kernel (is ...
Josh F's user avatar
  • 131
3 votes
3 answers
3k views

What would be a decent threshold for classification problem?

I'm using machine-learning algorithms to solve binary classification problem (i.e. classification can be 'good' or 'bad'). I'm using SVM based algorithms, ...
Ziv Levy's user avatar
  • 133
3 votes
1 answer
114 views

Training given pairs of similar values, not labels

I have pairs of "similar" values $(x_i, y_i)$ drawn from a space $x_i, y_i \in S$, and want to train a neural network $N$ such that $N(x_i)$ would be "close" to $N(y_i)$ for all $i$, yet, to make it ...
ybungalobill's user avatar
3 votes
1 answer
194 views

Using the appropriate machine learning algorithm [duplicate]

I am not sure if this is the right forum to ask this. I have some data of the houses, like their size(in square meters), if they use aircondition, how many residents live in, I have their electricity ...
user avatar
3 votes
2 answers
274 views

Language Classification + AWS ML: what am I doing wrong?

I'm evaluating Amazon's machine learning platform, and thought that I would give it a "simple" classification problem first. As a disclaimer, I am quite new to machine learning (hence my interest in ...
David Ferris's user avatar
3 votes
1 answer
602 views

How does a recurrent connection in a neural network work?

I am reading a very interesting paper on genetic algorithms which define neural networks. I am familiar with how a feedforward neural network operates, but then I came across this: Where node #4 ...
Zach's user avatar
  • 133
3 votes
1 answer
310 views

Specific case of a ranking model

So the problem I'm solving is this: I have a list of conversations of 3 messages each (for eg. "hi", " how are you", "remind me to fix this bug" is one conversation, and my problem will have many of ...
Mathguy's user avatar
  • 411
2 votes
1 answer
123 views

System Identification vs Machine Learning for dynamic system modelling

So I found another discussion regarding this, but the answers did not fully separate the differenced between SID and ML. Hopefully a discussion here can shed some light on some larger differences both ...
bullfighter's user avatar
2 votes
1 answer
80 views

prove that 2 collection have the same VC-dimensions

I'm new here on the site, I'm a final year student in computer science. In a machine learning course, there was a question on a test that I could not understand. The question goes like this: Suppose ...
hah's user avatar
  • 29
2 votes
1 answer
1k views

NeuroEvolution: NEAT algorithm innovation numbers

I have been reading up on the NeuronEvolution of Augmented Topologies and there's this little thing that's been bothering me. While reading Kenneth Stanley's Paper on NEAT I came on this figure here: ...
Rikku121's user avatar
  • 123