Computer Science Stack Exchange
Computer Science Stack Exchange

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
1220 questions
13
votes
5 answers

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 learning and system identification communities are faced…
CherryQu
  • 231
  • 2
  • 5
10
votes
2 answers

Which classifier is more accurate for a SVM classification?

I am learning the SVM classification and encounter a problem. I am not sure if this dilemma has a terminology for it. Assume we would like to classify patient by SVM given the samples of healthy people ( of both gender) and people with liver cancer…
Cassie
  • 305
  • 1
  • 5
9
votes
2 answers

What was going on before PAC learning

I am investigating PAC learning (computational learning theory) as a beginner with no previous knowledge of machine learning / AI. I am investigating the model mainly from a historical point of view. For this, the most important things are of course…
codd
  • 701
  • 3
  • 14
8
votes
1 answer

PAC learning model definition

The probably approximately correct (PAC) learning model is defined as: A concept class $C$ is said to be PAC-learnable if there exists an algorithm $A$ and a polynomial function $poly(·,·,·,·)$ such that for any $ε>0$ and $δ>0$, for all…
Asterion
  • 213
  • 1
  • 3
7
votes
3 answers

Do linearly dependent features in feature vectors improve the feature vector?

I was reading Wiki on feature vectors, and as far as I can see, it suggests creating new features from already existing features: Higher-level features can be obtained from already available features and added to the feature vector, for example…
The Unfun Cat
  • 1,793
  • 2
  • 16
  • 29
7
votes
1 answer

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 sample has 8 features (columns), one of which…
user2237160
  • 95
  • 1
  • 5
6
votes
2 answers

Mathematical proof for why gradient descent algorithm always converges

I am currently learning machine learning and I stumbled across gradient descent. I understand why the algorithm always converges to the global/local minimum when the learning rate is small enough in my head, but is there a mathematical proof? Thank…
mango
  • 63
  • 3
6
votes
1 answer

Unsupervised Learning: BCM or Oja's Rule

I am learning about unsupervised machine learning, and am a bit confused regarding different algorithms to update weights. So, I understand that both Oja's Rule and BCM can be used. In Oja's rule: dw/dt = k*x*y - w*y^2 Where x is the value at the…
MrD
  • 215
  • 1
  • 8
6
votes
1 answer

Why does the experience propagation rule for checkers work in Tom Mitchell's book?

In Tom Mitchell's book "Machine Learning", Chap.1, a checkers game is used to illustrate how machine learning can be applied solve problems. An experience propagation rule is described for iterative learning of a hypothesis. Suppose a game has been…
Strin
  • 1,505
  • 1
  • 11
  • 16
5
votes
1 answer

What learning algorithm is appropriate for predicting one time-series from another?

I have eye-tracking data on two subjects -- a teacher, and a student. It's in the form (x, y, time), so there is a series of these for each subject. What the teacher looks at influences what the student looks at. What method would I use to predict…
james hawthorne
  • 51
  • 2
5
votes
1 answer

k-armed bandit - index policies vs. Q-learning

I am interested in the stateless k-armed bandit problem, where an agent repeatedly chooses one of k independent arms, each with a different distribution of rewards, and tries to maximize its total reward. I read many papers about this subject, but I…
Erel Segal-Halevi
  • 5,994
  • 1
  • 23
  • 59
5
votes
3 answers

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 guaranteed convergence under certain conditions and…
Seanny123
  • 641
  • 8
  • 23
5
votes
1 answer

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 a linear SVM and linearly separable data X. We…
iensen
  • 153
  • 5
5
votes
0 answers

Axis aligned rectangles: why is A an ERM in the case of infinite domain?

I'm working on a problem 2.3a in Shalev-Shwartz/Ben-David's Machine learning textbook, which states: An axis aligned rectangle classifier in the plane is a classifier that assigns 1 to a point if and only if it is inside a certain rectangle.…
dTdt
  • 151
  • 2
4
votes
3 answers

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 in deeper layers?
do.
  • 143
  • 1
  • 1
  • 4
1
2 3
…
9 10