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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.

Machine learning provides computer algorithms that automatically discover patterns in data and make intelligent decisions from them.

Classic Problems:

  • Classification (e.g., supervised, unsupervised)
  • Regression
  • Density estimation
  • Sampling
  • Reinforcement Learning

Relevant Algorithms:

  • Principal component analysis (PCA)
  • Neural network
  • Support vector machine (SVM)
  • K-nearest neighbor
  • Bayesian networks
  • Gaussian mixture model (GMM)
  • Decision trees
  • Genetic algorithms
  • Simulated annealing
  • Boosting
  • Hidden Markov model (HMM)
  • Conditional Random Field (CRF)
  • Kalman filter
  • Particle filter
  • Gibbs sampling

Applications:

  • Computer vision (e.g, object tracking, gesture recognition)
  • Image recognition (e.g, face, gait, iris, handwriting)
  • Speech recognition
  • Speaker recognition
  • Natural language processing (NLP)
  • Music information retrieval (MIR)
  • Bioinformatics
  • Spam filtering
1239 questions
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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. The number of training samples needed to have a…
yters
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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 machine learning and data mining. Update: Is…
FossilizedCarlos
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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 trying to learn a minimal DFA with $n$ states,…
Artem Kaznatcheev
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What is Temperature in LSTM (and neural networks generally)?

One of the hyperparameters for LSTM networks is temperature. What is it?
Justin Shenk
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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-incremented/incremented from bad/good by bad or…
Friendly Person 44
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Why do neural networks seem to perform better with restrictions placed on their topology?

Fully connected (at least layer to layer with more than 2 hidden layers) backprop networks are universal learners. Unfortunately, they are often slow to learn and tend to over-fit or have awkward generalizations. From fooling around with these…
Artem Kaznatcheev
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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 categories are being used in order to "learn" the…
Prot
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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 good machine learning algorithms for this…
Maecky
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29
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4 answers

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. These users normally (excluding the edge cases in…
phwd
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Why can't we mimic a dog's ability to smell COVID?

As far as I can tell, we have invented tools and algorithm to: Detect a wider range of colors at a larger range than humans or any other animals on the planet Detect sound with wavelengths inaccessible to humans or most animals on the planet But…
jonjbar
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What combination of data structures efficiently stores discrete Bayesian networks?

I understand the theory behind Bayesian networks, and am wondering what it takes to build one in practice. Let's say for this example, that I have a Bayesian (directed) network of 100 discrete random variables; each variable can take one of up to 10…
rxmnnxfpvg
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Clustering of Songs (The Joe Walsh Problem)

The Eagles are a rock supergroup from the 70s and 80s, responsible for such classics as Hotel California. They have two quite distinctive sounds, one where guitarist Joe Walsh is present (for example, in Life in the Fast Lane) and one where he is…
Dave Clarke
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What is the difference between a Neural Network, a Deep Learning System and a Deep Belief Network?

What is the difference between a Neural Network, a Deep Learning System and a Deep Belief Network? As I recall your basic neural network is a 3 layers kinda thing, and I have had Deep Belief Systems described as being neural networks stacked on top…
Frames Catherine White
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Why are diploid (dominant/recessive) genes not used widely in genetic algorithms?

In most implementations of genetic algorithms, the focus is on crossover and mutation. But somehow, most of them leave out diploid (dominant/recessive) nature of genes. As far as my (limited) understanding goes dominant/recessive nature of genes is…
Shayan RC
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What is meant by the term "prior" in machine learning

I am new to machine learning. I have read several papers where they have employed deep learning for various applications and have used the term "prior" in most of the model design cases, say prior in human body pose estimation. Can someone explain…
Amy
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