Interactive machine learning and data analytics

@InCollection{Webb02,
Title = {Integrating Machine Learning with Knowledge Acquisition},
Author = {G. I. Webb},
Booktitle = {Expert Systems },
Publisher = {Academic Press},
Year = {2002},
Address = {San Diego, CA},
Editor = {C. T. Leondes},
Pages = {937-959},
Volume = {3},
Audit-trail = {23/8 waiting on permission to post PDF. Received permission and posted PDF},
Keywords = {Machine Learning with Knowledge Acquisition from Experts and Machine Learning},
Related = {interactive-machine-learning}
}

ABSTRACT 

@Article{WebbWellsZheng99,
Title = {An Experimental Evaluation of Integrating Machine Learning with Knowledge Acquisition},
Author = {G. I. Webb and J. Wells and Z. Zheng},
Journal = {Machine Learning},
Year = {1999},
Number = {1},
Pages = {5-24},
Volume = {35},
Abstract = {Machine learning and knowledge acquisition from experts have distinct capabilities that appear to complement one another. We report a study that demonstrates the integration of these approaches can both improve the accuracy of the developed knowledge base and reduce development time. In addition, we found that users expected the expert systems created through the integrated approach to have higher accuracy than those created without machine learning and rated the integrated approach less difficult to use. They also provided favorable evaluations of both the specific integrated software, system called The Knowledge Factory, and of the general value of machine learning for knowledge acquisition.},
Address = {Netherlands},
Audit-trail = {27/10/03 requested permission to post pp pdf. 28/10/03 Permission granted by Kluwer. PDF Posted 30/10/03},
Keywords = {Machine Learning with Knowledge Acquisition from Experts and Rule Learning},
Publisher = {Springer},
Related = {interactive-machine-learning}
}

ABSTRACT Machine learning and knowledge acquisition from experts have distinct capabilities that appear to complement one another. We report a study that demonstrates the integration of these approaches can both improve the accuracy of the developed knowledge base and reduce development time. In addition, we found that users expected the expert systems created through the integrated approach to have higher accuracy than those created without machine learning and rated the integrated approach less difficult to use. They also provided favorable evaluations of both the specific integrated software, system called The Knowledge Factory, and of the general value of machine learning for knowledge acquisition.

@InProceedings{WebbWells96,
Title = {An Experimental Evaluation of Integrating Machine Learning with Knowledge Acquisition Through Direct Interaction with Domain Experts},
Author = {G.I. Webb and J. Wells},
Booktitle = {Proceedings of the 1996 {Pacific} Knowledge Acquisition Workshop (PKAW'96)},
Year = {1996},
Address = {Sydney},
Editor = {P. Compton and R. Mizoguchi and H. Motada and T. Menzies},
Pages = {170-189},
Publisher = {UNSW Press},
Abstract = {Machine learning and knowledge acquisition from experts have distinct and apparently complementary knowledge acquisition capabilities. This study demonstrates that the integration of these approaches can both improve the accuracy of the knowledge base that is developed and reduce the time taken to develop it. The system studied, called The Knowledge Factory is distinguished by the manner in which it supports direct interaction with domain experts with little or no knowledge engineering expertise. The benefits reported relate to use by such users. In addition to the improved quality of the knowledge base, in questionnaire responses the users provided favourable evaluations of the integration of machine learning with knowledge acquisition within the system.},
Audit-trail = {Reconstructed paper posted April 2006},
Keywords = {Machine Learning with Knowledge Acquisition from Experts and Rule Learning},
Location = {Coogee, Sydney, Australia},
Related = {interactive-machine-learning}
}

ABSTRACT Machine learning and knowledge acquisition from experts have distinct and apparently complementary knowledge acquisition capabilities. This study demonstrates that the integration of these approaches can both improve the accuracy of the knowledge base that is developed and reduce the time taken to develop it. The system studied, called The Knowledge Factory is distinguished by the manner in which it supports direct interaction with domain experts with little or no knowledge engineering expertise. The benefits reported relate to use by such users. In addition to the improved quality of the knowledge base, in questionnaire responses the users provided favourable evaluations of the integration of machine learning with knowledge acquisition within the system.

@Article{Webb96a,
Title = {Integrating Machine Learning With Knowledge Acquisition Through Direct Interaction With Domain Experts},
Author = {G. I. Webb},
Journal = {Knowledge-Based Systems},
Year = {1996},
Pages = {253-266},
Volume = {9},
Abstract = {Knowledge elicitation from experts and empirical machine learning are two distinct approaches to knowledge acquisition with differing and mutually complementary capabilities. Learning apprentices have provided environments in which a knowledge engineer may collaborate with a machine learning system allowing, for a synergy between the complementary approaches. The Knowledge Factory is a knowledge acquisition environment that allows a domain expert to collaborate directly with a machine learning system without the need for assistance from a knowledge engineer. This requires a different form of environment to the learning apprentice. This paper describes techniques for supporting such interactions and their implementation in a knowledge acquisition environment called The Knowledge Factory.},
Audit-trail = {Link via Science Direct},
Keywords = {Machine Learning with Knowledge Acquisition from Experts and Rule Learning},
Publisher = {Elsevier},
Related = {interactive-machine-learning}
}

ABSTRACT Knowledge elicitation from experts and empirical machine learning are two distinct approaches to knowledge acquisition with differing and mutually complementary capabilities. Learning apprentices have provided environments in which a knowledge engineer may collaborate with a machine learning system allowing, for a synergy between the complementary approaches. The Knowledge Factory is a knowledge acquisition environment that allows a domain expert to collaborate directly with a machine learning system without the need for assistance from a knowledge engineer. This requires a different form of environment to the learning apprentice. This paper describes techniques for supporting such interactions and their implementation in a knowledge acquisition environment called The Knowledge Factory.

@InProceedings{WebbWells95,
Title = {Recent Progress in Machine-Expert Collaboration for Knowledge Acquisition},
Author = {G. I. Webb and J. Wells},
Booktitle = {Proceedings of the Eighth Australian Joint Conference on Artificial Intelligence (AI'95)},
Year = {1995},
Address = {Singapore},
Editor = {X. Yao},
Pages = {291-298},
Publisher = {World Scientific},
Abstract = {Knowledge acquisition remains one of the primary constraints on the development of expert systems. A number of researchers have explored methods for allowing a machine learning system to assist a knowledge engineer in knowledge acquisition. In contrast, we are exploring methods for enabling an expert to directly interact with a machine learning system to collaborate during knowledge acquisition. We report recent extensions to our methodology encompassing a revised model of the role of machine learning in knowledge acquisition; techniques for communication between a machine learning system and a domain expert and novel forms of assistance that a machine learning system may provide to an expert.},
Audit-trail = {*},
Keywords = {Machine Learning with Knowledge Acquisition from Experts},
Location = {Canberra, Australia},
Related = {interactive-machine-learning}
}

ABSTRACT Knowledge acquisition remains one of the primary constraints on the development of expert systems. A number of researchers have explored methods for allowing a machine learning system to assist a knowledge engineer in knowledge acquisition. In contrast, we are exploring methods for enabling an expert to directly interact with a machine learning system to collaborate during knowledge acquisition. We report recent extensions to our methodology encompassing a revised model of the role of machine learning in knowledge acquisition; techniques for communication between a machine learning system and a domain expert and novel forms of assistance that a machine learning system may provide to an expert.

@InProceedings{Webb93e,
Title = {Control, Capabilities and Communication: Three Key Issues for Machine-Expert Collaborative Knowledge Acquisition},
Author = {G. I. Webb},
Booktitle = {Proceedings (Complement) of the Seventh European Workshop on Knowledge Acquisition for Knowledge-based Systems (EWKA'93)},
Year = {1993},
Editor = {N. Aussenac and G. Boy and B. Gaines and M. Linster and J.G. Ganascia and Y. Kodratoff },
Pages = {263-275},
Publisher = {Springer-Verlag},
Abstract = {Machine learning and knowledge elicitation are different but complementary approaches to knowledge acquisition. On the face of it there are large potential gains to be reaped from the integration of these two knowledge acquisition techniques. Machine-expert collaborative knowledge acquisition combines these approaches by placing the machine learning system and the human expert as partners in the knowledge-acquisition task. This paper examines three key issues facing machine-expert collaborative knowledge-acquisition where should control reside, what capabilities should each partner bring to the task and how should the partners communicate? },
Audit-trail = {*},
Keywords = {Machine Learning with Knowledge Acquisition from Experts},
Location = {Toulouse, France},
Related = {interactive-machine-learning}
}

ABSTRACT Machine learning and knowledge elicitation are different but complementary approaches to knowledge acquisition. On the face of it there are large potential gains to be reaped from the integration of these two knowledge acquisition techniques. Machine-expert collaborative knowledge acquisition combines these approaches by placing the machine learning system and the human expert as partners in the knowledge-acquisition task. This paper examines three key issues facing machine-expert collaborative knowledge-acquisition where should control reside, what capabilities should each partner bring to the task and how should the partners communicate?

@InProceedings{Webb92,
Title = {Man-Machine Collaboration for Knowledge Acquisition},
Author = {G. I. Webb},
Booktitle = {Proceedings of the Fifth Australian Joint Conference on Artificial Intelligence (AI'92)},
Year = {1992},
Address = {Singapore},
Editor = {A. Adams and L. Sterling},
Pages = {329-334},
Publisher = {World Scientific},
Abstract = {Both machine learning and knowledge elicitation from human experts have unique strengths and weaknesses. Man-machine collaboration for knowledge acquisition allows both knowledge acquisition techniques to be employed hand- in-hand. The strengths of each can alleviate the other's weaknesses. This has the potential to both reduce the time taken to develop an expert system while increasing the quality of the finished product. This paper discusses techniques for man-machine collaboration for knowledge acquisition and describes Einstein, a computer system that implements those techniques},
Audit-trail = {*},
Keywords = {Machine Learning with Knowledge Acquisition from Experts},
Location = {Hobart, Tas., Australia},
Related = {interactive-machine-learning}
}

ABSTRACT Both machine learning and knowledge elicitation from human experts have unique strengths and weaknesses. Man-machine collaboration for knowledge acquisition allows both knowledge acquisition techniques to be employed hand- in-hand. The strengths of each can alleviate the other's weaknesses. This has the potential to both reduce the time taken to develop an expert system while increasing the quality of the finished product. This paper discusses techniques for man-machine collaboration for knowledge acquisition and describes Einstein, a computer system that implements those techniques

@InProceedings{Webb91c,
Title = {Einstein: An Interactive Inductive Knowledge-Acquisition Tool},
Author = {G. I. Webb},
Booktitle = {Proceedings of the Sixth Banff Knowledge Acquisition for Knowledge-Based Systems Workshop},
Year = {1991},
Pages = {(36)1-16},
Abstract = {Einstein is a knowledge acquisition system that incorporates data-driven inductive rule development and refinement in a user driven production rule development and evaluation environment. This allows the user and the induction system to interact as a cooperative knowledge acquisition team. Unique features of this system include efficient automated inductive refinement of existing production rules, interactive user management of machine learning facilities, including local and global guidance, interactive specification of key examples and counter-examples and interactive case-based rule assessment.},
Audit-trail = {*},
Keywords = {Machine Learning with Knowledge Acquisition from Experts},
Location = {Banff, Canada},
Related = {interactive-machine-learning}
}

ABSTRACT Einstein is a knowledge acquisition system that incorporates data-driven inductive rule development and refinement in a user driven production rule development and evaluation environment. This allows the user and the induction system to interact as a cooperative knowledge acquisition team. Unique features of this system include efficient automated inductive refinement of existing production rules, interactive user management of machine learning facilities, including local and global guidance, interactive specification of key examples and counter-examples and interactive case-based rule assessment.