4
votes
Accepted
Convolutional Neural Network Feature Engineering?
Your questions phrasing is a bit confusing. I'll try to answer my interpretation of your confusion.
I think the base of your confusion relays on the assumption that 2D convolution receives a 2 ...
- 174
3
votes
Why is it not always possible to compute the centroid of feature vectors?
A metric space consists of a set $X$ of "points" and a metric $d\colon X \times X \to \mathbb{R}_{\geq 0}$ (giving the "distance" between any two points) which satisfies the following constraints:
...
- 274k
2
votes
Accepted
Why do AlphaGo and AlphaGo Zero include board history in the input features
"This doesn't seem to be discussed in either of the papers, "
Yes, it is discussed in at least one of the papers.
Here is an excerpt taken from Mastering the Game of Go without Human Knowledge - ...
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2
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Why do AlphaGo and AlphaGo Zero include board history in the input features
The primary reason for including a history of states is likely indeed the ko rule. Even if having a long history will often be redundant, it's unlikely to hurt either (except that it might take some ...
- 295
2
votes
What is the difference between 'features' and 'descriptors' in computer vision / machine learning?
The SIFT descriptor vector is a feature vector. "Descriptor vector" and "feature vector" are synonyms in this context. Most of the descriptions of SIFT I've seen use the phrase "descriptor vector", ...
D.W.♦
- 154k
2
votes
Accepted
How are HOGs calculated?
First, let's talk about what a histogram of directions is. You can think of the image as a 2D discrete function of x and y: I(x,y). You can take partial derivatives of this function: Ix and Iy. So at ...
- 568
2
votes
Use of Activation Units in Facial Expression Understanding
As a preliminary answer, I can't imagine the extraction of AUs affecting their ability to detect emotions, since according to Do Deep Neural Networks Learn Facial Action Units When Doing Expression ...
- 641
2
votes
Accepted
Classification training data, but regression prediction
This is still a binary classification task. In the abstract, there are two ways to handle this:
Most classifiers can output a predicted class and a confidence score (which indicates how confident ...
D.W.♦
- 154k
2
votes
Accepted
Largest isomorphic subgraphs of two graphs with features
This looks like it is at least as hard as the induced subgraph isomorphism problem. Suppose that all $w$ vectors are 0. Then your question is identical to asking whether for a maximal induced ...
D.W.♦
- 154k
1
vote
Accepted
How can a Machine Learning model predict this classification problem?
Your premise is wrong. You are assuming we don't know how to compute the features on new sentences. But we do know.
We construct features so that we can compute the features on any sentence ...
D.W.♦
- 154k
1
vote
characteristic vectors for systems
Yes. You want to look at the bag-of-words model and n-gram models.
The bag-of-words model corresponds to the part of your characteristic vector: namely, the parts ...
D.W.♦
- 154k
1
vote
Terminology: Difference between decision variables, features and attributes in this paper?
Feature and attribute have both the same meaning.
Decision variable is the variable which is used to make a split in a decision tree in tree based algorithms, i.e. the variable on which the decision ...
- 123
1
vote
What is the official name of a specific type of combination algorithm
You are probably looking for subsets of a set. If a set has $n$ elements, it has $2^n$ subsets when counting also the empty set (there is a clear correspondence to all bit strings of length $n$). So ...
- 22.4k
1
vote
Problem with Understanding "correlated attributes into a set of values of uncorrelated attributes" in PCA
It looks like you are using RapidMiner documentation as a medium to study. If that is case, then the best answer to your question and probably to some other questions of yours should be, I believe, a ...
- 38.5k
1
vote
Featurizing images of different dimensions
Option 1: You can crop the images to the smallest sizes in all dimensions. However, blindly cropping images will cause you to lose important information, if you don't have a region of interest. For ...
- 507
1
vote
Computer vision methods without "pre-training"
Yup, there are lots of them. For example, image segmentation through graph cuts, camera calibration, image morphing, image stitching, reconstruction of a 3D scene from multiple 2D images, optical ...
D.W.♦
- 154k
1
vote
Accepted
Feature selections : Still a high dimentionality
There seems to be a misconception. You can use $k$-nearest neighbors with any number of dimensions. There is no prohibition on using it with a large number of features. And in some cases $k-NN will ...
D.W.♦
- 154k
1
vote
Computation of normalized first derivative in discrete case
As you have noticed, the denominators in the definition of $x'_t,y'_t$ ultimately have no effect, as they will be normalized away.
Nonetheless, they do have significance. This definition ensures ...
D.W.♦
- 154k
1
vote
Computation of normalized first derivative in discrete case
For the first part of your question, the significance is just to guarantee that the vector $(\hat{x}'_t,\hat{y}'_t)$ has length $1$. This means that it tells us in which direction $(x_t,y_t)$ is ...
- 81.4k
1
vote
How to detect Farkas or MPEG4 FDP points on image with a face?
In the simplified special case where each image $x$ has a single correct labelling $k=\kappa(x)$, this becomes just a regression problem: you want to find a function $q$ that minimizes the risk
$$\...
D.W.♦
- 154k
1
vote
Accepted
How does an image classifier select relevant features?
There is no single answer. A cascade is a very simple idea: it basically represents a bunch of classifiers, applied sequentially.
You are free to decide how each individual cascade will work. You ...
D.W.♦
- 154k
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