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Results tagged with machine-learning
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user 4446
For questions related to machine learning (ML), which is a set of methods that can automatically detect patterns in data, and then use the uncovered patterns to predict future data, or to perform other kinds of decision making under uncertainty (such as planning how to collect more data). ML is usually divided into supervised, unsupervised and reinforcement learning. Deep learning is a subfield of ML that uses deep artificial neural networks.
2
votes
What's the threshold to call something 'machine learning'?
T. Mitchell defines machine learning in "Machine Learning" book as
a computer program is said to learn from experience 𝐸 concerning some class of tasks 𝑇 and performance measure 𝑃, if its performa …
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0
votes
Why does PCA work well while the total variance retained is small?
Because it selects both Xtrain and Xtest from the space of two selected principal components. Hence, the 90% accuracy is in that 2-D selected space.
This fact that the ratio in PCA stands the inform …
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0
votes
What would be the steps to create an sentiment analysis chatbot?
There are some different approaches to learning this sequential acting. First, you can use RL (reinforcement learning) and define some rewards over the action of the user over that question, to explor …
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1
vote
What is a "learned emulator"?
In a typical situation, for the emulation of physical environments, you need to define all physical rules and forces. In the "learned emulators", they use some machine learning techniques to learn tho …
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3
votes
2
answers
577
views
How can active learning be used in the case of complex models that require a lot of data?
We have a series of data and we want to label the parts of each series. As we do not have any training data, we could try to use active learning as a solution, but the problem is that our classifier i …
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3
votes
Accepted
How can active learning be used in the case of complex models that require a lot of data?
As I found this case backs to the sequence labeling. Sequence labeling has some classic solution such as conditional random fields (CRFs) and hidden Markov model (HMM). Also, have some solution in Act …
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0
votes
How could I compute in real-time the similarity between tickets?
You need to create an active learning loop over the process of the learning. Try to start from a history of tickets and using doc2vec to get the similarity. When you find a bad result in the result of …
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2
votes
What kind of problems cannot be solved using machine learning techniques?
At least you should be aware of two points:
P/NP/NP-hard (and all other class of complexities) are thoroughly valid for the machine learning area as well. Because these concepts are related to the f …
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0
votes
What consequence would a polynomial time algorithm for SAT have on AGI?
It seems this post could be an answer to your question. In sum, it says that AGI is more related to interactional complexity than classical complexity. Therefore, these two are perpendicular concepts …
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1
vote
Accepted
In the machine learning literature, what does it mean to say that something is "embedded" in...
Embedding is the process of representing data (from a source domain) in a new (or target) domain. Usually, the source domain is discrete, and the target domain is continuous. For example, embedding wo …
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0
votes
How is regression machine learning?
It is just a statistical technique that is used in machine learning and it depends on the nature of the machine learning problem. I think you should be referred to the relation of the statistics and m …
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5
votes
Accepted
What does "semantic gap" mean?
In terms of transfer learning, semantic gap means different meanings and purposes behind the same syntax between two or more domains. For example, suppose that we have a deep learning application to d …
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0
votes
Accepted
Do other online/incremental algorithms not suffer from catastrophic forgetting?
To enlighten this, you need to understand the cause of the catastrophic forgetting. Fundamentally, the cause is an overlap in representations of different aspects of data in the learning model [Using …
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1
vote
How did the variance and double summation of the covariance come to the L2 minimization equa...
As in the second line, the first two terms are $\mathbb{E}_{\hat{y}}$, it means the variable of the expectation is $\hat{y}_i$ and you can take out $y_i$s from $\mathbb{E}_{\hat{y}}$s. Now we can use …
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1
vote
How to understand the GCN equation?
$\tilde{A}$ is related to normalized Laplacian matrix that "shows many useful properties" of matrix $A$. Note that:
Since the degree matrix $D$ is diagonal, its reciprocal square root $D^{-{\frac {1} …
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