{"id":2008,"date":"2016-11-11T18:39:55","date_gmt":"2016-11-11T18:39:55","guid":{"rendered":"http:\/\/www.trivedigaurav.com\/blog\/?p=2008"},"modified":"2020-08-25T11:44:12","modified_gmt":"2020-08-25T11:44:12","slug":"on-interactive-machine-learning","status":"publish","type":"post","link":"https:\/\/www.trivedigaurav.com\/blog\/on-interactive-machine-learning\/","title":{"rendered":"On Interactive Machine Learning"},"content":{"rendered":"
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When talking about machine learning, you may encounter many terminologies such as such as “online learning,” “active learning,” and \u201chuman in the loop” methods. Here are some of my thoughts on the relationship between interactive machine learning and machine learning in general. This is an extract from my answers to my comprehensive exam<\/a>.
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Traditionally machine-learning has been classified into supervised<\/em> and unsupervised<\/em> learning families. In supervised learning the training data, \\mathcal{D}, consists of N sets of feature vectors each with a desired label provided by a teacher:<\/p>\n<\/div>\n<\/div>\n<\/div>\n

Training Set\u00a0 \\hspace{10pt} \\mathcal{D} = \\{(\\textbf{x}_i, y_i)\\}_{i=1}^{N}<\/p>\n

where, \\textbf{x}_i \\in \\mathcal{X} is a d-dimensional feature vector<\/p>\n

and y_i \\in \\mathcal{Y} is the known label for it<\/p>\n

The task is to learn a function, f : \\mathcal{X} \\to \\mathcal{Y}, which can be used on unseen data.<\/p>\n

In unsupervised learning, our data consists of vectors \\textbf{x}_i, but no target label y_i. Common tasks under this category include clustering, density estimation and discovering patterns. A combination of these two is called semi-supervised<\/em> learning, which has a mixture of labeled and unlabeled data in the training set. The algorithm assigns labels for missing data points using certain similarity measures.<\/p>\n

While researchers are actively looking at improving the unsupervised learning techniques, supervised machine learning has been the dominant form of learning till date. However, traditional supervised algorithms assume that we have training data along with the labels readily available. They are not concerned with the process of obtaining the target values y_is for the training dataset. Often, obtaining labelled data is one of the main bottlenecks in applying these techniques in domain specific applications. Further, current approaches do not provide easy mechanisms for the end-users to correct problems when models deviate from the desired learning concept. NLP models are often built by experts in linguistics and\/or machine learning, with limited or no scope for the end-users to provide input. Here the domain experts, or the end-users, provide input to models as annotations for a large batch of training data. This approach can be expensive, inefficient and even infeasible in many situations. This includes many problems in the clinical domain such as building models for analyzing EMR data.<\/p>\n

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\u201cHuman-in-the-loop\u201d algorithms may be able to leverage the capabilities of a domain expert during the learning process. These algorithms can optimize their learning behavior through interaction with humans. Interactive Machine Learning (IML) is a subset of this class of algorithms. It is defined as the process of building machine learning models iteratively through end-user input. It allows the users to review model outputs and make corrections by giving feedback for building revised models. The users are then able to see model changes and verify them. This feedback loop allows end-users to refine the models further with every iteration. Some early examples for this definition include applications in image segmentation<\/a>, interactive document clustering<\/a>, document retrieval<\/a>, bug triaging<\/a> and even music composition<\/a>. You can read more about this in the article titled \"Power to the People: The Role of Humans in Interactive Machine Learning\"<\/em> (Amershi et.al., 2014).<\/p>\n<\/div>\n<\/div>\n<\/div>\n

Interactive machine learning builds on a variety of styles of learning algorithms:<\/p>\n