Advanced Search
DSpace@MIT

Browsing Computer Science and Artificial Intelligence Lab (CSAIL) by Title

Research and Teaching Output of the MIT Community

Browsing Computer Science and Artificial Intelligence Lab (CSAIL) by Title

Sort by: Order: Results:

  • Winston, Patrick H. (1982-05-01)
    This paper is a synthesis of several sets of ideas: ideas about learning from precedents and exercises, ideas about learning using near misses, ideas about generalizing if-then rules, and ideas about using censors to ...
  • Winston, Patrick H. (1977-01-01)
    Learning is defined to be the computation done by a student when there is a transfer of information to him from a teacher. In the particular kind of learning discussed, the teacher names a source and destination. In ...
  • Winston, Patrick H. (1978-01-01)
    In the particular kind of learning discussed in this paper, the teacher names a destination and a source. In the sentence, "Robbie is like a fox," Robbie is the destination and fox is the source. The student, on analyzing ...
  • Hall, Robert Joseph (1986-05-01)
    Explanation-based Generalization requires that the learner obtain an explanation of why a precedent exemplifies a concept. It is, therefore, useless if the system fails to find this explanation. However, it is not ...
  • Beal, Jacob (2007-08-23)
    Human intelligence is a product of cooperation among many different specialists. Much of this cooperation must be learned, but we do not yet have a mechanism that explains how this might happen for the "high-level" agile ...
  • Shih, Lawrence; Karger, David (2003-05-01)
    Trees are a common way of organizing large amounts of information by placing items with similar characteristics near one another in the tree. We introduce a classification problem where a given tree structure gives us ...
  • Stamatoiu, Oana L. (2004-05-18)
    If we are to understand how we can build machines capable of broad purpose learning and reasoning, we must first aim to build systems that can represent, acquire, and reason about the kinds of commonsense knowledge that ...
  • Stamatoiu, Oana L. (2004-05-18)
    If we are to understand how we can build machines capable of broadpurpose learning and reasoning, we must first aim to build systemsthat can represent, acquire, and reason about the kinds of commonsenseknowledge that we ...
  • Masquelier, Timothee; Serre, Thomas; Thorpe, Simon; Poggio, Tomaso (2007-12-26)
    One of the most striking feature of the cortex is its ability to wire itself. Understanding how the visual cortex wires up through development and how visual experience refines connections into adulthood is a key question ...
  • Iba, Glenn A. (1979-09-01)
    This work proposes a theory for machine learning of disjunctive concepts. The paradigm followed is one of teaching and testing, where the teaching is accomplished by presenting a sequence of positive and negative ...
  • Meila, Marina; Jordan, Michael I. (1995-11-01)
    Compliant control is a standard method for performing fine manipulation tasks, like grasping and assembly, but it requires estimation of the state of contact between the robot arm and the objects involved. Here we present ...
  • Maron, Oded (1998-12-01)
    There are many learning problems for which the examples given by the teacher are ambiguously labeled. In this thesis, we will examine one framework of learning from ambiguous examples known as Multiple-Instance learning. ...
  • Ghahramani, Zoubin; Jordan, Michael I. (1995-01-24)
    Real-world learning tasks often involve high-dimensional data sets with complex patterns of missing features. In this paper we review the problem of learning from incomplete data from two statistical perspectives---the ...
  • Beal, Jacob (2005-04-13)
    Examples are a powerful tool for teaching both humans and computers.In order to learn from examples, however, a student must first extractthe examples from its stream of perception. Snapshot learning is ageneral approach ...
  • Leibo, Joel Z; Mutch, Jim; Rosasco, Lorenzo; Ullman, Shimon; Poggio, Tomaso (2010-12-30)
    Invariance to various transformations is key to object recognition but existing definitions of invariance are somewhat confusing while discussions of invariance are often confused. In this report, we provide an operational ...
  • Aycinena, Meg; Kaelbling, Leslie Pack; Lozano-Perez, Tomas (2008-02-25)
    Many object recognition systems are limited by their inability to share common parts or structure among related object classes. This capability is desirable because it allows information about parts and relationships in ...
  • Olshausen, Bruno A. (1996-12-01)
    In previous work (Olshausen & Field 1996), an algorithm was described for learning linear sparse codes which, when trained on natural images, produces a set of basis functions that are spatially localized, oriented, ...
  • Winston, Patrick H. (1981-11-01)
    Much Learning is done by way of studying precedents and exercises. A teacher supplies a story, gives a problem, and expects a student both to solve a problem and to discover a principle. The student must find the ...
  • Miller, Erik G.; Tieu, Kinh; Stauffer, Chris P. (2001-09-01)
    We present a unifying framework in which "object-independent" modes of variation are learned from continuous-time data such as video sequences. These modes of variation can be used as "generators" to produce a manifold of ...
  • Ross, Michael G.; Kaelbling, Leslie Pack (2003-09-08)
    This memo describes the initial results of a project to create aself-supervised algorithm for learning object segmentation from videodata. Developmental psychology and computational experience havedemonstrated that the ...
MIT-Mirage