Talks

• Training a model using a support vector machine (SVM) [ talk ]

  Given a binary classification problem and a dataset known to have label noise what is the best way to use a limited number of human resources to manually validate and correct the data? This is a very common problem setting in a variety of problem domains and in this talk we introduce a technique that is both theoretically well founded and produces good results in many of our datasets. The essence of the technique is to train a model using a support vector machine (SVM) and then rank the data points based on their distance to the separating hyperplane. Experimental results on a number of real world datasets will be presented and the focus will be on applying this technique to real world scenarios.

  Speaker

  Nikhil Ketkar is a Director of Engineering at Indix where he leads the machine learning team. Ex-Team lead of the data mining team in Guavus, a startup doing big data analytics in the telecom domain.

  LinkedIn

• Machine Learning [ talk ]

  'Machine Learning' (ML) is relevant across the vertical industry segments and ML's significance is deep rooted. Armed with delivery channels such as Cloud, Mobile etc. ML is making a massive sense towards every form of data i.e., Structured/Unstructured Text, Video, Audio, Image etc. The current session intends to share couple of practical Enterprise use-cases while helping towards a right approach to identify a right technique/algorithm (towards addressing a specific business problem).

  Speaker

  Dr G Subrahmanya VRK Rao (Dr Rao) strives to address business/technology problems by help building enterprise-class adaptive systems.

  LinkedIn