Who Killed Jackie Bates? Murder and Mercy during the Great Depression (review) (2009)
The Canadian Historical Review - Volume 90, Number 3, September 2009
Avrim Blum, Tom Mitchell, Saif Mohammad, Lecturer Rich Maclin, Human Annotation
� Learn classifier from annotated training data � Apply classifier on unseen test data � Larger the training set � More accurate is the classifier � Consider Word Sense Disambiguation �...
John Ramish, Advised Prof, Tom Mitchell
In the history of AI, there have been many different knowledge representation languages for different purposes: production systems for fine-grain control, logics for propositions, frames for...
Yanxin Shi, Michael Klustein, Itamar Simon, Tom Mitchell, Ziv Bar-joseph
Motivation: When analyzing expression experiments, researchers are often interested in identifying the set of biological processes that are up-or down-regulated under the experimental condition...
Dissertation: New Theoretical Frameworks for Machine Learning. (2008)
Maiden Name Popa, Maria-florina Balcan, Avrim Blum, Manuel Blum, Yishay Mansour, Tom Mitchell, ...
Dependable Computing and Fault Tolerance (TSF) Team, CNRS, Toulouse, France.
Four-Factor Structure of the Correctional Personnel Rating Scale (2008)
Tom Mitchell, Gunna J. Yun, Erica Pinkos
Canadian Journal of Criminology and Criminal Justice - Volume 50, Number 2, April/avril 2008
BIOINFORMATICS ORIGINAL PAPER doi:10.1093/bioinformatics/btl676 Systems biology (2008)
Yanxin Shi, Tom Mitchell, Ziv Bar-joseph
Inferring pairwise regulatory relationships from multiple time series datasets
Dissertation: New Theoretical Frameworks for Machine Learning. (2008)
Maiden Name Popa, Maria-florina Balcan, Avrim Blum, Manuel Blum, Yishay Mansour, Tom Mitchell, ...
Dependable Computing and Fault Tolerance (TSF) Team, CNRS, Toulouse, France.
Mark Craven, Dan Dipasquo, Dayne Freitag, Andrew Mccallum, Tom Mitchell, Kamal Nigam, ...
The World Wide Web is a vast source of information accessible to computers, but understandable only to humans. The goal of the research described here is to automatically create a computer...
New Theoretical Frameworks for Machine Learning (2008)
Maria-florina Balcan, Manuel Blum, Yishay Mansour, Tom Mitchell, Santosh Vempala
This thesis has two primary thrusts. The first is developing new models and algorithms for important modern and classic learning problems. The second is establishing new connections between Machine...
WebWatcher: Knowledge Navigation in the World Wide Web (2007)
Dayne Freitag, Thorsten Joachims, Tom Mitchell
Many have noted the need for software to assist people in locating information on the World Wide Web. Although effective tools exist, they typically rely on brute-force scanning and indexing of Web...
Principal Investigators (2007)
William Whirtaker, Taka Kanade, Tom Mitchell
0 1991 Carnegie Mellon University
Kamal Nigam, Andrew Mccallum, Sebastian Thrun, Tom Mitchell
This paper shows that the accuracy of learned text classifiers can be improved by augmenting small numbers of labeled training documents with a large pool of unlabeled documents. This is significant...
Andrew Mccallum, Ronald Rosenfeld, Tom Mitchell, Andrew Ng
When documents are organized in a large number of topic categories, the categories are often arranged in a hierarchy. The U.S. patent database and Yahoo are two examples. This paper shows that the...
MACHINE LEARNING An Artificial Intelligence Approach Contributing authors: (2007)
John Anderson, Ranan Banerji, Gary Bradshaw, Jaime Carbonell, Thomas Dietterich, Norman Haas, ...
by
Thorsten Joachims, Dayne Freitag, Tom Mitchell
notation thereon. Views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either
Continuous hidden process model for time series expression experiments (2007)
Shi, Yanxin, Klustein, Michael, Simon, Itamar, Mitchell, Tom, Bar-Joseph, Ziv
Motivation: When analyzing expression experiments, researchers are often interested in identifying the set of biological processes that are up-or down-regulated under the experimental condition...
Inferring pairwise regulatory relationships from multiple time series datasets (2007)
Shi, Yanxin, Mitchell, Tom, Bar-Joseph, Ziv
Motivation: Time series expression experiments have emerged as a popular method for studying a wide range of biological systems under a variety of conditions. One advantage of such data is the...
New Theoretical Frameworks for Machine Learning (2007)
Maria-florina Balcan, Manuel Blum, Yishay Mansour, Tom Mitchell, Santosh Vempala
This thesis develops and analyzes theoretical frameworks for new emerging paradigms of Machine Learning including Semi-supervised, Active, and Similarity-based Learning. These are areas of...
Computational Aspects of Preference Aggregation (2006)
Vincent Conitzer, Avrim Blum, Tom Mitchell
IIS-0427858, IIS-0234695, and IIS-0121678, as well as a Sloan Fellowship awarded to Tuomas Sandholm, and an IBM Ph.D. Fellowship. The views and conclusions contained in this document are those of the...
Computational Aspects of Preference Aggregation (2006)
Vincent Conitzer, Avrim Blum, Tom Mitchell
IIS-0427858, IIS-0234695, and IIS-0121678, as well as a Sloan Fellowship awarded to Tuomas Sandholm, and an IBM Ph.D. Fellowship. The views and conclusions contained in this document are those of the...
Patrick Riley, Tom Mitchell, Jack Mostow, Patrick Riley, Patrick Riley, Patrick Riley, ...
(in specified language) Coaching?
Detecting significant multidimensional spatial clusters (2005)
Daniel B. Neill, Andrew W. Moore, Francisco Pereira, Tom Mitchell
Assume a uniform, multidimensional grid of bivariate data, where each cell of the grid has a count ci and a baseline bi. Our goal is to find spatial regions (d-dimensional rectangles) where the ci...
Coaching: Learning and Using Environment and (2005)
Agent Models For, Tom Mitchell, Jack Mostow, Patrick Riley, Patrick Riley
State and Action Spaces . . . . . . . . . . . . . . . . . . . . . . 52 4.3.1 Tree Factored Representation . . . . . . . . . . . . . . . . . . . 52 4.3.2 Soccer State Spaces . . . . . . . . . . . . ....
Detecting Significant Multidimensional Spatial (2005)
Clusters Daniel Neill, Daniel B. Neill, Andrew W. Moore, Francisco Pereira, Tom Mitchell
Assume a uniform, multidimensional grid of bivariate data, where each cell of the grid has a count c i and a baseline b i . Our goal is to find spatial regions (d-dimensional rectangles) where the c...
Coaching: Learning and Using Environment and Agent Models for Advice (2005)
Tom Mitchell, Jack Mostow, Patrick Riley, Patrick Riley
Fellowship. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or...
Methods for detection of spatial and spatio-temporal clusters (2005)
Daniel B. Neill, Tom Mitchell, Jeff Schneider
the NSF or the U.S. government.
Ryan Shaun Baker, Albert T. Corbett, Kenneth R. Koedinger, Shelley Evenson, Tom Mitchell
Latent Response Models, intelligent agents 2 Students use intelligent tutors and other types of interactive learning environments in a considerable variety of ways. In this thesis, I detail my work...
Computer Based Testing of Medical Knowledge (2003)
Mitchell, Tom, Aldridge, Nicola, Williamson, Walter, Broomhead, Peter
This is a conference paper.
Computer Based Testing of Medical Knowledge (2003)
Mitchell, Tom, Aldridge, Nicola, Williamson, Walter, Broomhead, Peter
This is a conference paper.
Active Learning for Information Extraction with Multiple View (2003)
Feature Sets Rosie, Rosie Jones, Tom Mitchell, Ellen Rilo
A major problem with machine learning approaches to information extraction is the high cost of collecting labeled examples. Active learning seeks to make ecient use of a labeler 's time by...
The ballistics of micro-particles into the mucosa and skin / (2002)
BLDSC reference no.: D223021.
Towards robust computerised marking of free-text responses (2002)
Mitchell, Tom, Russell, Terry, Broomhead, Peter, Aldridge, Nicola
This is a conference paper.
Machine learning of fMRI virtual sensors of cognitive states (2002)
Tom Mitchell, Rebecca Hutchinson, Marcel Just, Sharlene Newman, Radu Stefan, Niculescu Francisco, ...
Is it feasible to train classifiers to decode the cognitive state of a human subject, based on single-episode fMRI data? If so, these trained classifiers could be used as virtual sensors to detect...
Machine Learning of fMRI Virtual Sensors of (2002)
Cognitive States Tom, Tom Mitchell, Rebecca Hutchinson, Marcel Just, Sharlene Newman, Radu Stefan, ...
Is it feasible to train classifiers to decode the cognitive state of a human subject, based on single-episode fMRI data? If so, these trained classifiers could be used as virtual sensors to detect...
Detecting Cognitive States Using Machine Learning (2002)
Very little is known about the relationship between the cognitive states and the fMRI data, and very little is known about the feasibility of training classifiers to decode cognitive states.
Machine Learning of fMRI Virtual Sensors of Cognitive States (2002)
Tom Mitchell, Rebecca Hutchinson, Marcel Just, Sharlene Newman, Radu Stefan Niculescu, Francisco Pereira, ...
Is it feasible to train classifiers to decode the cognitive state of a human subject, based on single-episode fMRI data? If so, these trained classifiers could be used as virtual sensors to detect...
Autonomous Planetary Rover at Carnegie Mellon (2000)
Whittaker, William, Kanade, Takeo, Mitchell, Tom
This report describes progress in research on a six-legged autonomous robot, called the Ambler, designed for planetary exploration. Much of the research focused on understanding the capabilities of...
Text classification from labeled and unlabeled documents using EM (2000)
Andrew Kachites Mccallum, Tom Mitchell
zy
Discovering Test Set Regularities in Relational Domains (2000)
Machine learning typically involves discovering regularities in a training set, then applying these learned regularities to classify objects in a test set. In this paper we present an approach to...
Learning to Construct Knowledge Bases from the World Wide Web (2000)
Mark Craven, Dayne Freitag, Andrew Mccallum, Tom Mitchell, Seán Slattery, ...
The World Wide Web is a vast source of information accessible to computers, but understandable only to humans. The goal of the research described here is to automatically create a computer...
Machine Learning, , 1--34 () (2000)
Text Classification From, Kamal Nigam, Andrew Kachites Mccallum, Tom Mitchell, W. Cohen
This paper shows that the accuracy of learned text classifiers can be improved by augmenting a small number of labeled training documents with a large pool of unlabeled documents. This is important...
Discovering Test Set Regularities in Relational Domains (2000)
Sean Slattery Sean, Tom Mitchell
Machine learning typically involves discovering regularities in a training set, then applying these learned regularities to classify objects in a test set. In this paper we present an approach to...
Sebastian Thrun, Christos Faloutsos, Tom Mitchell, Larry Wasserman
Part 1: Main Summary 3 1 Introduction The field of automated learning and discovery--often called data mining, machine learning, oradvanced data analysis--is currently undergoing a revolution. The...
Sebastian Thrun, Christos Faloutsos, Tom Mitchell, Larry Wasserman
This report summarizes the CONALD meeting, which took place June 11-13, 1998, at Carnegie Mellon University. CONALD brought together an interdisciplinary group of scientists, concerned with decision...
Sebastian Thrun, Christos Faloutsos, Tom Mitchell, Larry Wasserman
The field of automated learning and discovery---often called data mining, machine learning, or advanced data analysis---is currently undergoing a major change. The progressing computerization of...
Sebastian Thrun, Christos Faloutsos, Tom Mitchell, Larry Wasserman
This report summarizes the CONALD meeting, which took place June 11-13, 1998, at Carnegie Mellon University. CONALD brought together an interdisciplinary group of scientists, concerned with decision...
Learning to Construct Knowledge Bases from the World Wide Web (1999)
Mark Craven, Dan Dipasquo, Dayne Freitag, Andrew Mccallum, Tom Mitchell, Kamal Nigam, ...
The World Wide Web is a vast source of information accessible to computers, but understandable only to humans. The goal of the research described here is to automatically create a computer...
Learning to Construct Knowledge Bases from the World Wide Web (1999)
Mark Craven, Dan Dipasquo, Dayne Freitag, Andrew Mccallum, Tom Mitchell, Kamal Nigam
The World Wide Web is a vast source of information accessible to computers, but understandable only to humans. The goal of the research described here is to automatically create a computer...
Text Classification from Labeled and Unlabeled Documents using EM (1999)
Kamal Nigam, Andrew Mccallum, Sebastian Thrun, Tom Mitchell
This paper shows that the accuracy of learned text classifiers can be improved by augmenting a small number of labeled training documents with a large pool of unlabeled documents. This is important...
Sebastian Thrun, Christos Faloutsos, Tom Mitchell, Larry Wasserman
This report summarizes the CONALD meeting, which took place June 11-13, 1998, at Carnegie Mellon University. CONALD brought together an interdisciplinary group of scientists, concerned with decision...
Sebastian Thrun, Christos Faloutsos, Tom Mitchell, Larry Wasserman
This report summarizes the CONALD meeting, which took place June 11-13, 1998, at Carnegie Mellon University. CONALD brought together an interdisciplinary group of scientists, concerned with decision...
Sebastian Thrun, Christos Faloutsos, Tom Mitchell, Larry Wasserman
This report summarizes the CONALD meeting, which took place June 11-13, 1998, at Carnegie Mellon University. CONALD brought together an interdisciplinary group of scientists, concerned with decision...
Year End Report: Autonomous Planetary Rover at Carnegie Mellon, 1989. (1998)
Whittaker, William, Kanade, Takeo, Mitchell, Tom
The Carnegie Mellon University program to develop an Earth-based prototype of an autonomous planetary rover is organized around three teams that are developing the locomotion, perception, and...
Using EM to Classify Text from Labeled and Unlabeled Documents (1998)
Nigam, Kamal, McCallum, Andrew, Thrun, Sebastian, Mitchell, Tom
This paper shows that the accuracy of learned text classifiers can be improved by augmenting a small number of labeled training documents with a large pool of unlabeled documents. This is significant...
Learning to Extract Symbolic Knowledge from the World Wide Web (1998)
Craven, Mark, McCallum, Andrew, PiPasquo, Dan, Mitchell, Tom, Freitag, Dayne
The World Wide Web is a vast source of information accessible to computers, but understandable only to humans. The goal of the research described here is to automatically create a computer...
The Dynamics of Cognition: An ACT-R Model of Cognitive Arithmetic. (1998)
Lebiere, Christian, Anderson, John R., John, Bonnie, Mitchell, Tom
Cognitive arithmetic, the study of the mental representation of numbers and arithmetic facts and the processes that create, access and manipulate them, offers a unique window into human cognition....
Combining labeled and unlabeled data with co-training (1998)
We consider the problem of using a large unlabeled sample to boost performance of a learning algorithm when only a small set of labeled examples is available. In particular, we consider a setting in...
Combining labeled and unlabeled data with co-training (1998)
avrim+Qcs.cmu.edu We consider the problem of using a large unla-beled sample to boost performance of a learn-ing algorit,hrn when only a small set of labeled examples is available. In particular, we...
Combining labeled and unlabeled data with co-training (1998)
We consider the problem of using a large unlabeled sample to boost performance of a learning algorithm when only a small set of labeled examples is available. In particular, we consider a problem...
Report on the CONALD Workshop on Learning from Text and the (1998)
Jaime Carbonell, Mark Craven, Steve Fienberg, Tom Mitchell, Yiming Yang
An increasing fraction of the world's information and data is now represented in textual form. For example, the World Wide Web, online news feeds, and other Internet sources contain a tremendous...
Combining Labeled and Unlabeled Data with Co-Training (1998)
We consider the problem of using a large unlabeled sample to boost performance of a learning algorithm when only a small set of labeled examples is available. In particular, we consider a problem...
Learning to Classify Text from Labeled and Unlabeled Documents (1998)
Kamal Nigam, Andrew Mccallum, Sebastian Thrun, Tom Mitchell
In many important text classification problems, acquiring class labels for training documents is costly, while gathering large quantities of unlabeled data is cheap. This paper shows that the...
Learning to Extract Symbolic Knowledge from the World Wide Web (1998)
Mark Craven, Dan Dipasquo, Dayne Freitag, Andrew Mccallum, Tom Mitchell, Kamal Nigam, ...
The World Wide Web is a vast source of information accessible to computers, but understandable only to humans. The goal of the research described here is to automatically create a computer...
Improving Text Classification by Shrinkage in a Hierarchy of Classes (1998)
Andrew Mccallum, Ronald Rosenfeld, Tom Mitchell, Andrew Y. Ng
When documents are organized in a large number of topic categories, the categories are often arranged in a hierarchy. The U.S. patent database and Yahoo are two examples. This paper shows that the...
Combining Labeled and Unlabeled Data with Co-Training (1998)
We consider the problem of using a large unlabeled sample to boost performance of a learning algorithm when only a small set of labeled examples is available. In particular, we consider a setting in...
Sebastian Thrun, Christos Faloutsos, Tom Mitchell, Larry Wasserman
This report summarizes the CONALD meeting, which took place June 11-13, 1998, at Carnegie Mellon University. CONALD brought together an interdisciplinary group of scientists, concerned with decision...
Sebastian Thrun Christos, Christos Faloutsos, Tom Mitchell, Larry Wasserman
This report summarizes the CONALD meeting, which took place June 11-13, 1998, at Carnegie Mellon University. CONALD brought together an interdisciplinary group of scientists, concerned with decision...
Learning to Classify Text from Labeled and Unlabeled Documents (1998)
Kamal Nigam, Andrew Mccallum, Sebastian Thrun, Tom Mitchell
In many important text classification problems, acquiring class labels for training documents is costly, while gathering large quantities of unlabeled data is cheap. This paper shows that the...
A Case Study in Using Linguistic Phrases for Text Categorization on the WWW (1998)
Johannes Fürnkranz, Tom Mitchell, Ellen Riloff
Most learning algorithms that are applied to text categorization problems rely on a bag-of-words document representation, i.e., each word occurring in the document is considered as a separate...
Improving Text Classification by Shrinkage in a Hierarchy of Classes (1998)
Andrew Mccallum, Ronald Rosenfeld, Tom Mitchell, Andrew Y. Ng
When documents are organized in a large number of topic categories, the categories are often arranged in a hierarchy. The U.S. patent database and Yahoo are two examples. This paper shows that the...
Learning to Extract Symbolic Knowledge from the World Wide Web (1998)
Mark Craven, Dayne Freitag, Andrew Mccallum, Tom Mitchell, Kamal Nigam, ...
The goal of the Web-KB project is to develop automatic methods for constructing and maintaining large knowledge bases whose contents mirror those of the World Wide Web. We argue for the feasibility...
Using EM to Classify Text from Labeled and Unlabeled Documents (1998)
Kamal Nigam, Andrew Mccallum, Sebastian Thrun, Tom Mitchell
This paper shows that the accuracy of learned text classifiers can be improved by augmenting a small number of labeled training documents with a large pool of unlabeled documents. This is significant...
Learning to Extract Symbolic Knowledge from the World Wide Web (1998)
Mark Craven, Dan Dipasquo, Dayne Freitag, Andrew Mccallum, Tom Mitchell, Kamal Nigam, ...
The World Wide Web is a vast source of information accessible to computers, but understandable only to humans. The goal of the research described here is to automatically create a computer...
Learning to Extract Symbolic Knowledge from the World Wide Web (1998)
Mark Craven, Dan Dipasquo, Dayne Freitag, Andrew Mccallum, Tom Mitchell, Kamal Nigam
The World Wide Web is a vast source of information accessible to computers, but understandable only to humans. The goal of the research described here is to automatically create a computer...
A Probabilistic Method for Tracking a Vocalist (1998)
Lorin V. Grubb, Tom Mitchell, Jack Mostow
When a musician gives a recital or concert, the music performed generally includes accompaniment. To render a good performance, the soloist and the accompanist must know the musical score and must...
Combining labeled and unlabeled data with co-training (1998)
We consider the problem of using a large unlabeled sample to boost performance of a learning algorithm when only a small set of labeled examples is available. In particular, we consider a problem...
A Probabilistic Method for Tracking a Vocalist (1998)
Lorin V. Grubb, Tom Mitchell, Jack Mostow
When a musician gives a recital or concert, the music performed generally includes accompaniment. To render a good performance, the soloist and the accompanist must know the musical score and must...
Learning to extract symbolic knowledge from the World Wide Web (1998)
Mark Craven, Dan Dipasquo, Dayne Freitag, Andrew Mccallum, Tom Mitchell, Kamal Nigam, ...
The World Wide Web is a vast source of information accessible to computers, but understandable only to humans. The goal of the research described here is to automatically create a computer...
A Framework and Toolkit for the Construction of Multimodal Learning Interfaces (1998)
Minh Tue Vo, Bonnie John, Tom Mitchell
document are those of the author and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of the DARPA or the U.S. Government....
WebWatcher: A Tour Guide for the World Wide Web (1997)
Thorsten Joachims, Dayne Freitag, Tom Mitchell
We explore the notion of a tour guide software agent for assisting users browsing the World Wide Web. A Web tour guide agent provides assistance similar to that provided by a human tour guide in a...
WebWatcher: A Learning Apprentice for the World Wide Web (1997)
Robert Armstrong, Dayne Freitag, Thorsten Joachims, Tom Mitchell
We describe an information seeking assistant for the world wide web. This agent, called WebWatcher, interactively helps users locate desired information by employing learned knowledge about which...
WebWatcher: A Tour Guide for the World Wide Web (1997)
Thorsten Joachims, Dayne Freitag, Tom Mitchell
1 We explore the notion of a tour guide software agent for assisting users browsing the World Wide Web. A Web tour guide agent provides assistance similar to that provided by a human tour guide in a...
Learning in Information Agents (1997)
Tom Mitchell, Learn User Interests, Ffl Newsweeder Wisewire
00 training articles from each Learn: ffl To classify which newsgroup a new article is from Carnegie Mellon 6 Twenty NewsGroups comp.graphics misc.forsale comp.os.ms-windows.misc rec.autos...
Believable Agents: Building Interactive Personalities (1997)
A. Bryan Loyall, Joseph Bates, Tom Mitchell
not be interpreted as representing the official policies, either expressed or implied, of the sponsoring organizations or any other parties.
WebWatcher: A Tour Guide for the World Wide Web (1997)
Thorsten Joachims, Dayne Freitag, Tom Mitchell
We explore the notion of a tour guide software agent for assisting users browsing the World Wide Web. A Web tour guide agent provides assistance similar to that provided by ahuman tour guide in a...
Rich Caruana, Shumeet Baluja, Tom Mitchell
A patient visits the doctor; the doctor reviews the patient's history, asks questions, makes basic measurements (blood pressure, ...), and prescribes tests or treatment. The prescribed course of...
Learning Evaluation Functions (1996)
Justin A. Boyan, Scott E. Fahlman, Tom Mitchell
Evaluation functions are an essential component of practical search algorithms for optimization, planning and control. Examples of such algorithms include hillclimbing, simulated annealing,...
Explanation-Based Learning for Mobile Robot Perception (1996)
Explanation-based neural network learning (EBNN) has recently been introduced as a method for reducing the amount of training data required for reliable generalization, by relying instead on...
Rich Caruana, Shumeet Baluja, Tom Mitchell
A patient visits the doctor; the doctor reviews the patient's history, asks questions, makes basic measurements (blood pressure,...), and prescribes tests or treatment. The prescribed course of...
Rich Caruana, Shumeet Baluja, Tom Mitchell
A patient visits the doctor; the doctor reviews the patient's history, asks questions, makes basic measurements (blood pressure,...), and prescribes tests or treatment. The prescribed course of...
Webwatcher: A learning apprentice for the world wide web (1995)
Robert Armstrong, Dayne Freitag, Thorsten Joachims, Tom Mitchell
We describe an information seeking as-sistant for the world wide web. This agent, called WebWatcher, interactively helps users locate desired information by employing learned knowledge about which...
Webwatcher: A learning apprentice for the world wide web (1995)
Robert Armstrong, Dayne Freitag, Thorsten Joachims, Tom Mitchell
We describe an information seeking assistant for the world wide web. This agent, called WebWatcher, interactively helps users locate desired information by employing learned knowledge about which...
WebWatcher: A Learning Apprentice for the World Wide Web (1995)
Robert Armstrong, Dayne Freitag, Thorsten Joachims, Tom Mitchell
We describe an information seeking assistant for the world wide web. This agent, called WebWatcher, interactively helps users locate desired information by employing learned knowledge about which...
WebWatcher: Machine Learning and Hypertext (1995)
Thorsten Joachims, Tom Mitchell, Dayne Freitag, Robert Armstrong
This paper describes the first implementation of WebWatcher, a Learning Apprentice for the World Wide Web. We also explore the possibility of extracting information from the structure of hypertext....
The Prospective Student's Introduction to the Robot Learning Problem (1995)
The paper presents an introduction to the area of learning in robot control. Fundamental principles are discussed, learning mechanisms such as reinforcement learning are discussed, and open questions...
WebWatcher: Machine Learning and Hypertext (1995)
Thorsten Joachims, Tom Mitchell, Dayne Freitag, Robert Armstrong
This paper describes the rst implementation of WebWatcher, a Learning Apprentice for the World Wide Web. We also explore the possibility of extracting information from the structure of hypertext. We...
Webwatcher: A learning apprentice for the world wide web (1995)
Robert Armstrong, Dayne Freitag, Thorsten Joachims, Tom Mitchell
We describe an information seeking assistant for the world wide web. This agent, called WebWatcher, interactively helps users locate desired information by employing learned knowledge about which...
Experience With a Learning Personal Assistant (1994)
Tom Mitchell, Rich Caruana, Dayne Freitag, John McDermott, David Zabowski
Personal software assistants that help users with tasks like finding information, scheduling calendars, or managing work-flow will require significant customization to each individual user. For...
Explanation Based Learning: A Comparison of Symbolic and Neural Network Approaches (1993)
Tom Mitchell, Sebastian B. Thrun
Explanation based learning has typically been considered a symbolic learning method. An explanation based learning method that utilizes purely neural network representations (called EBNN) has...
Learning for Coordination of Vision and Action (1992)
Salganicoff, Marcos, Bajcsy, Ruzena, Mitchell, Tom
We define the problem of visuomotor coordination and identify bottleneck problems in the implementation of general purpose vision and action systems. We conjecture that machine learning methods...
A personal learning apprentice (1992)
Lisa Dent, Jesus Boticario, John Mcdermott, Tom Mitchell, David Zabowski
Abstract requesting certain types of meetings. In order to be Personalized knowledge-based systems have not yet become widespread, despite their potential for valuable assistance in many daily tasks....
Interfaces that Learn: A Learning Apprentice for Calendar Management (1991)
Jean Jourdan, Lisa Dent, John McDermott, Tom Mitchell, David Zabowski
this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the U.S. Government or of Digital Equipment Corporation....
William Whittaker, Takeo Kanade, Tom Mitchell
contained in this document are those of the authors and should not be interpreted as representing
Principal Investigators (1989)
Takeo Kanade, Tom Mitchell, William Whittaker
@ 1989 Carnegie Mellon University..--.-,I,--This research was sponsored by NASA under Contract NAGW-1175. The views and con-clusions contained in this document arc those of the authors and should not...
Using EM to classify text from labeled and unlabeled documents (1521)
Kamal Nigam, Andrew Mccallum, Sebastian Thrun, Tom Mitchell
This paper shows that the accuracy of learned text classifiers can be improved by augmenting a small number of labeled training documents with a large pool of unlabeled documents. This is significant...
Activation changes the spectrum but not the diversity of genes expressed by T cells
Teague, T. Kent, Hildeman, David, Kedl, Ross M., Mitchell, Tom, Rees, William, Schaefer, Brian C., ...
During activation T cells are thought to change their patterns of gene expression dramatically. To find out whether this is true for T cells activated in animals, the patterns of genes expressed in...
Eradication of Cryptosporidium parvum Infection by Mice with Ovalbumin-Specific T Cells
Lukin, Kara, Cosyns, Mary, Mitchell, Tom, Saffry, Milton, Hayward, Anthony
CD154 is necessary for mice to clear a Cryptosporidium parvum infection, but whether this ligand has to be expressed on T cells with specificity for C. parvum has not been determined. We infected...
Activation changes the spectrum but not the diversity of genes expressed by T cells
Teague, T. Kent, Hildeman, David, Kedl, Ross M., Mitchell, Tom, Rees, William, Schaefer, Brian C., ...
During activation T cells are thought to change their patterns of gene expression dramatically. To find out whether this is true for T cells activated in animals, the patterns of genes expressed in...
Eradication of Cryptosporidium parvum Infection by Mice with Ovalbumin-Specific T Cells
Lukin, Kara, Cosyns, Mary, Mitchell, Tom, Saffry, Milton, Hayward, Anthony
CD154 is necessary for mice to clear a Cryptosporidium parvum infection, but whether this ligand has to be expressed on T cells with specificity for C. parvum has not been determined. We infected...
Type I Interferons Keep Activated T Cells Alive
Marrack, Philippa, Kappler, John, Mitchell, Tom
Antigen injection into animals causes antigen-specific T cells to become activated and, rapidly thereafter, die. This antigen-induced death is inhibited by inflammation. To find out how inflammation...
Teague, T. Kent, Schaefer, Brian C., Hildeman, David, Bender, Jeremy, Mitchell, Tom, Kappler, John W., ...
The cytokines interleukin (IL)-2, IL-4, IL-6, IL-7, and IL-15 have all previously been shown to inhibit resting T cell death in vitro. We have found a difference in the response of T cells to IL-6,...
Bender, Jeremy, Mitchell, Tom, Kappler, John, Marrack, Philippa
We investigated the mechanism by which α/β T cells expand upon transfer to T cell–deficient host mice by injecting carboxyfluorescein diacetate succinimidyl ester–labeled T cells into mice...
T Cells Compete for Access to Antigen-Bearing Antigen-Presenting Cells
Kedl, Ross M., Rees, William A., Hildeman, David A., Schaefer, Brian, Mitchell, Tom, Kappler, John, ...
These studies tested whether antigenic competition between T cells occurs. We generated CD8+ T cell responses in H-2b mice against the dominant ovalbumin epitope SIINFEKL (ova8) and subdominant...