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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11
votes
2answers
2k views

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 $L(M) \subseteq \Sigma^*\setminus B$?...
3
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1answer
102 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 ...
98
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7answers
20k 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. ...
28
votes
1answer
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 ...
46
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5answers
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 ...
29
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4answers
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. ...
4
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3answers
7k views

What is the "spatial information" in convolutional neural network

deep learning research papers always claim that deeper layers of CNN have good "semantic information" but poor "spatial information". What is the spatial information exactly. Is that some activations ...
19
votes
3answers
8k 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 ...
4
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4answers
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, ...
3
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2answers
939 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. ...
1
vote
1answer
285 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 ...
34
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2answers
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 ...
29
votes
4answers
70k 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 ...
28
votes
12answers
9k 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-...
8
votes
1answer
11k 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 (...
9
votes
2answers
3k 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 ...
5
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1answer
6k 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)= \...
10
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4answers
5k 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. ...
8
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1answer
8k 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 ...
6
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2answers
8k 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 ...
2
votes
1answer
73 views

What is the Best and easiest way to create a Classifer for Sentiment Analysis [closed]

Sentiment analysis using Machine Learning is a hot topic. In the present situation when a person doesn't have a problem in having the training data set then which way should we create the classifier ...
1
vote
5answers
639 views

Google's Deep Dreamer

I was just wondering on a more technical side, if anyone could explain what Google does to create these amazing images from it's deep dream system. Could anyone explain to me in a step by step way, ...
6
votes
2answers
453 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}{\...
6
votes
4answers
7k 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 ...
5
votes
1answer
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, ...
3
votes
2answers
267 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 ...
3
votes
1answer
477 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 ...
2
votes
2answers
1k views

Why is automatically labeling data in unsupervised learning hard?

I currently studying machine learning and pattern recognition area. Today, my professor said implementing an unsupervised system that automatically labels data is difficult. Why is that? I think if I ...
2
votes
0answers
75 views

Using the random forest algorithm to predict vectors [duplicate]

I know this might sound like a newbie question, but bear with me. I have read a paper where researchers use a random forest to predict species distribution, but in their study, they only predict a ...
12
votes
1answer
331 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 ...
6
votes
1answer
3k 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 ...
5
votes
1answer
269 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 ...
5
votes
1answer
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 ...
4
votes
4answers
4k 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 ...
4
votes
1answer
372 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? (...
4
votes
2answers
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 ...
4
votes
3answers
324 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 ...
3
votes
1answer
186 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 ...
3
votes
2answers
144 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,...
3
votes
1answer
2k 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 ...
3
votes
0answers
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 ...
3
votes
1answer
269 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 ...
2
votes
2answers
811 views

How to handle missing continuous attribute values in ID3 (Iterative Dichotomiser 3)?

I'm implementing the ID3 algorithm (Iterative Dichotomiser 3). I have an attribute which happens to be continuous like 12.21, 3.01, etc. AND have missing values which are marked as "NA". How I'm ...
2
votes
3answers
1k 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 ...
1
vote
1answer
991 views

How to identify labels in unsupervised learning?

Let's say I am working on handwritten digit recognition (0 to 9). I know for instance that if I use clustering then I need to look for 10 clusters. But once I have the 10 clusters,how do I identify ...
6
votes
2answers
251 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 ...
5
votes
1answer
175 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 ...
3
votes
1answer
124 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) = ...
3
votes
1answer
101 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 ...
3
votes
1answer
561 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 ...