Zobrazeno 1 - 10
of 141
pro vyhledávání: '"Kovalerchuk, Boris"'
Autor:
Williams, Alice, Kovalerchuk, Boris
Insufficient amounts of available training data is a critical challenge for both development and deployment of artificial intelligence and machine learning (AI/ML) models. This paper proposes a unified approach to both synthetic data generation (SDG)
Externí odkaz:
http://arxiv.org/abs/2409.02079
Autor:
Martinez, Joshua, Kovalerchuk, Boris
Interpretable interactive visual pattern discovery in lossless 3D visualization is a promising way to advance machine learning. It enables end users who are not data scientists to take control of the model development process as a self-service. It is
Externí odkaz:
http://arxiv.org/abs/2403.13014
Autor:
Kovalerchuk, Boris, McCoy, Elijah
Building accurate and interpretable Machine Learning (ML) models for heterogeneous/mixed data is a long-standing challenge for algorithms designed for numeric data. This work focuses on developing numeric coding schemes for non-numeric attributes for
Externí odkaz:
http://arxiv.org/abs/2305.18437
Autor:
Hayes, Dustin, Kovalerchuk, Boris
This work uses visual knowledge discovery in parallel coordinates to advance methods of interpretable machine learning. The graphic data representation in parallel coordinates made the concepts of hypercubes and hyperblocks (HBs) simple to understand
Externí odkaz:
http://arxiv.org/abs/2305.18434
Understanding black-box Machine Learning methods on multidimensional data is a key challenge in Machine Learning. While many powerful Machine Learning methods already exist, these methods are often unexplainable or perform poorly on complex data. Thi
Externí odkaz:
http://arxiv.org/abs/2305.18429
Autor:
Kovalerchuk, Boris, Phan, Hoang
This study explores a new methodology for machine learning classification tasks in 2-dimensional visualization space (2-D ML) using Visual knowledge Discovery in lossless General Line Coordinates. It is shown that this is a full machine learning appr
Externí odkaz:
http://arxiv.org/abs/2305.19132
Autor:
Kovalerchuk, Boris, McCoy, Elijah
Developing Machine Learning (ML) algorithms for heterogeneous/mixed data is a longstanding problem. Many ML algorithms are not applicable to mixed data, which include numeric and non-numeric data, text, graphs and so on to generate interpretable mode
Externí odkaz:
http://arxiv.org/abs/2206.06476
Visualization of Machine Learning (ML) models is an important part of the ML process to enhance the interpretability and prediction accuracy of the ML models. This paper proposes a new method SPC-DT to visualize the Decision Tree (DT) as interpretabl
Externí odkaz:
http://arxiv.org/abs/2205.04035