Questions tagged [domain-adaptation]

The tag has no usage guidance.

Filter by
Sorted by
Tagged with
1
vote
0answers
67 views

Using PCA in domain adaptation

In literature, I see people using (Kernelized) Principle Component Analysis, not for feature extraction, but for domain adaptation. In other words, I have data from a source domain and I would like to ...
0
votes
0answers
18 views

Do we need pretrained blocks for domain adversarial training

I have a question related to the following classic paper on domain-adversarial training: Ganin, Y., Ustinova, E., Ajakan, H., Germain, P., Larochelle, H., Laviolette, F., Marchand, M. and Lempitsky, ...
0
votes
0answers
53 views

How is 'domain adaptation' different from 'exploiting' multiple domains?

I am asking this question from context of transfer learning paradigm of machine learning. In transfer learning, we are given different domains one of which is a target domain and others, the ...
2
votes
0answers
51 views

How to integrate expert knowledge to outlier detection algorithms?

Suppose I have a dataset of 20 features, X1, X2..X20. ...
0
votes
1answer
38 views

ML methods for cold start - domain adaptation

Imagine a scenario: You work with credit card transactions and you use ML to assign probabilities to each transaction to be fraudulent or not. You operate in different countries and you have ML models ...
0
votes
1answer
115 views

Testing the scope of application of a logistic regression model

My aim is to assess whether I can apply a logistic regression that was fitted on a sample A (where I have explanatory variables and the outcomes) to a different sample B where I don't know the ...
1
vote
1answer
31 views

Robust machine learning for slightly different class proportions in multiple data sets

Say we have n similar data sets, with the same variables, and outcome labels x and y. In these data sets, domains slightly differ as suggested by the proportion of the minority class x (ranging from 1%...
0
votes
0answers
63 views

Domain knowledge or data dredging?

I have a binary classification problem where I'm required to classify transactions as anomalous or normal (1 or 0 respectively), with anomalies being the rarer instance. With what I know to be true ...
3
votes
2answers
785 views

Learn a mapping between two datasets using Neural Network

I have two matrices $A_1$ of size $N\times K$ and $A_2$ of size $M\times K$ which contain data and every row has a corresponding label $y \in {1, 2, 3}$. I want to learn a mapping between those two ...
1
vote
1answer
99 views

Ratio of training/testing data different to real life?

My company wants to build a model that will be used to predictive conversion that is usually about 2%. However every sample we purchase (converted or unconverted) is expensive. So my question is: How ...
9
votes
2answers
15k views

Maximum Mean Discrepancy (distance distribution)

I have two data sets ( source and target data) which follow the different distribution. I am using MMD - that is a non-parametric distance distribution- to compute marginal distribution between the ...
4
votes
3answers
1k views

regression with constraints

I have some domain knowledge I want to use in a regression problem. Problem statement The dependent variable $y$ is continuous. The independent variables are $x_1$ and $x_2$. Variable $x_1$ is ...
15
votes
5answers
12k views

What is difference between 'transfer learning' and 'domain adaptation'?

Is there any difference between 'transfer learning' and 'domain adaptation'? I don't know about context, but my understanding is that we have some dataset 1 and train on it, after which we have ...
0
votes
1answer
575 views

Can an unstandarized Beta distribution have a negative domain? [duplicate]

(*Question edited for clarification) Does the lower bound of the unstandarized beta distribution always have to be bigger than 0?
1
vote
0answers
31 views

What description fits this this learning scenario, is this a form of active learning?

A system is involved in dialog with a human partner. The system has a model of a problem domain and knows mappings between words an concepts. The human has its own model of the problem domain and ...
4
votes
0answers
77 views

What are some adaptive machine learning techniques that cater for data that may change slightly but is still correct? [closed]

Are there suitable machine learning techniques that may be applied to a continual stream of data and update its models for data that it believes to be different to the most representative case but ...
2
votes
2answers
93 views

Biased classification because of data from different sites?

Working in neuroscience, we often classify data from different sites. Usually I balance my data for sites - if I have for instance to classify the data for some illness vs. normal health condition, ...
4
votes
1answer
944 views

Frustratingly Easy Domain Adaptation

I refer to the paper by called Frustratingly Easy Domain Adaptation (http://www.umiacs.umd.edu/~hal/docs/daume07easyadapt.pdf) where the feature space of both the source and target data are augmented ...
5
votes
1answer
503 views

What are the most popular domain adaptation methods (for transfer learning)?

I understand supervised and unsupervised learning well, and would be able to identify some 'basic' examples of, for example, supervised classifcation as: SVMs Random Forests Logistic Regression ...