Toward Actionable Support Vector Machines: A Ranking-based Approach

During the last decade, Support Vector Machines (SVMs) have attracted a great deal of attention and achieved huge success mainly as powerful classifiers. However, one of the main drawbacks of this learning method is the lack of intelligibility of the results. SVMs are "black box" systems that do not provide insights on the reasons of a classification or explanations - the results produced must be taken on faith. We are concerned about the problem of intelligibility because from our practical experience, domain experts strongly prefer Machine Learning with explanations rather than a black box even if the black box system achieves a high predictive performance. In that context, we have developed a new approach to provide explanations and make SVMs results more actionable. The underlying idea is to produce explanations by applying symbolic Machine Learning models to SVM-produced ranking results. More precisely, we are contrasting SVM results from the top and bottom of rankings to detect the main discriminative properties between classes which can be quite useful for the practitioner to direct actions and understand the system. We applied our approach on several datasets. Our empirical results seem very promising and show the utility of our methodology with regard to the intelligibility and actionability of an SVM output

Toward Actionable Support Vector Machines: A Ranking-based Approach

During the last decade, Support Vector Machines (SVMs) have attracted a great deal of attention and achieved huge success mainly as powerful classifiers. However, one of the main drawbacks of this learning method is the lack of intelligibility of the results. SVMs are "black box" systems that do not provide insights on the reasons of a classification or explanations - the results produced must be taken on faith. We are concerned about the problem of intelligibility because from our practical experience, domain experts strongly prefer Machine Learning with explanations rather than a black box even if the black box system achieves a high predictive performance. In that context, we have developed a new approach to provide explanations and make SVMs results more actionable. The underlying idea is to produce explanations by applying symbolic Machine Learning models to SVM-produced ranking results. More precisely, we are contrasting SVM results from the top and bottom of rankings to detect the main discriminative properties between classes which can be quite useful for the practitioner to direct actions and understand the system. We applied our approach on several datasets. Our empirical results seem very promising and show the utility of our methodology with regard to the intelligibility and actionability of an SVM output

Generating Semantic Description from Drawings of Scenes with Shadows

This report reproduces a thesis of the same title submitted to the Department of Electrical Engineering, Massachusetts Institute of Technology, in partial fulfillment of the requirements for the degree of Doctor of Philosophy, September 1972.The research reported here concerns the principles used to automatically generate three-dimensional representations from line drawings of scenes. The computer programs involved look at scenes which consist of polyhedra and which may contain shadows and various kinds of coincidentally aligned scene features. Each generated description includes information about edge shape (convex, concave, occluding, shadow, etc.), about decomposition of the scene into bodies, about the type of illumination for each region (illuminated, projected shadow, or oriented away from the light source), and about the spacial orientation of regions. The methods used are based on the labeling schemes of Huffman and Clowes; this research provides a considerable extension to their work and also gives theoretical explanation to the heuristic scene analysis work of Guzman, Winston, and others.MIT Artificial Intelligence Laborator

Understanding Scenes With Shadows

Work reported herein was conducted at the Artificial Intelligence Laboratory, a Massachusetts Institute of Technology research program supported by the Advanced Research Projects Agency of the Department of Defense, and was monitored by the Office of Naval Research under Contract Number N00014-70-A-0362-0002.The basic problem of this research is to find methods which will enable a program to construct a three dimensional interpretation from the line drawing of a scene, where the scene may have shadows and various degeneracies. These methods differ from those used in earlier related programs in that they use region information extensively, and include formalisms for eye and lighting position. The eventual result of this research will be a program which should be able to successfully treat scenes with far fewer restrictions than present programs will tolerate.MIT Artificial Intelligence Laboratory Vision Group Department of Defense Advanced Research Projects Agenc

Understanding and Representing Natural Language Meaning

Coordinated Science Laboratory was formerly known as Control Systems LaboratoryOffice of Naval Research / N00014-75-C-061

Age and Growth of King Mackerel, \u3cem\u3eScomberomorus cavalla\u3c/em\u3e, from the Atlantic Coast of the United States

Whole sagittae from 683 and sectioned sagittae from 773 adult (age\u3e 0 ; 437-1.310 mm FL), and lapilli from 29 larval (2-7 mm SL) and 69 young-of-the-year (79-320 mm FL) king mackerel, were examined. All fish were from waters off the Atlantic coast of the southeastern United States (Cape Canaveral, Florida to Cape Fear. North Carolina). Back-calculated lengths at ages and von Bertalanffy growth equations were calculated from both whole and sectioned sagittae. Ages determined from sectioned sagittae were significantly greater than ages determined from whole sagittae, and the magnitude of the difference increased with age (from sections). Rings on sectioned sagittae are considered to be true annual increments, forming during June-September. There was no clear pattern to ring formation on whole otoliths. The oldest fish examined was age 21. The daily nature of rings on lapilli of age 0 king mackerel was not validated, but if the marks are formed daily they suggest growth rates of approximately 0.47 mm/d for early larvae and 2.9 mm/d for fish 1-3 months of age

An Expert Distributed Robotics System with Comprehension and Learning Abilities in the Aircraft Flight Domain

Coordinated Science Laboratory was formerly known as Control Systems LaboratoryAir Force Office of Scientific Research / F49620-82-K-000

Age and growth of king mackerel, Scomberomorus Cavalla, from the Atlantic coast of the United States.

Whole sagittae from 683 and sectioned sagittae from 773 "adult" (age> 0 ; 437-1.310 mm FL) and lapiUi from 29 larval (2-7 nun SL) and 69 young-of-the-year (79-320 mm FL) king mackerel. were examined. All fish were from waters off the Atlantic coast of the southeastern United States (Cape Canaveral, Florida to Cape Fear. North Carolina). Back-calculated lengths at ages and von Bertalanffy growth equations were calculated from both whole and sectioned sagittae. Ages determined from sectioned sagittae were significantly greater than ages determined from whole sagittae, and the magnitude of the difference increased with age (from sections). Rings on sectioned sagittae are considered to be true annual increments. fonning during June-September. There was no clear pattern to ring formation on whole otoliths. The oldest fish examined was age 21. The daily nature of rings on lapilli of age 0 king mackerel was not validated. but if the marks are formed daily they suggest growth rates of approximately 0.47 mm/d for early larvae and 2.9 mmld for fish 1-3 months of age

Understanding and Representing Natural Language Meaning

Coordinated Science Laboratory was formerly known as Control Systems LaboratoryOffice of Naval Research / N00014-75-C-061