Thomas G. Dietterich

Bio: Thomas G. Dietterich is an academic researcher from Oregon State University. The author has contributed to research in topics: Reinforcement learning & Markov decision process. The author has an hindex of 74, co-authored 279 publications receiving 51935 citations. Previous affiliations of Thomas G. Dietterich include University of Wyoming & Stanford University.

Machine Learning for Sequential Data: A Review

Abstract: Statistical learning problems in many fields involve sequential data. This paper formalizes the principal learning tasks and describes the methods that have been developed within the machine learning research community for addressing these problems. These methods include sliding window methods, recurrent sliding windows, hidden Markov models, conditional random fields, and graph transformer networks. The paper also discusses some open research issues.

Overfitting and undercomputing in machine learning

Abstract: A central problem in machine learning is supervised learning—that is, learning from labeled training data. For example, a learning system for medical diagnosis might be trained with examples of patients whose case records (medical tests, clinical observations) and diagnoses were known. The task of the learning system is to infer a function that predicts the diagnosis of a patient from his or her case records. The function to be learned might be represented as a set of rules, a decision tree, a Bayes network, or a neural network. Learning algorithms essentially operate by searching some space of functions (usually called the hypothesis class) for a function that fits the given data. Because there are usually exponentially many functions, this search cannot actually examine individual hypothesis functions but instead must use some more direct method of constructing the hypothesis functions from the data. This search can usually be formalized by defining an objective function (e.g., number of data points predicted incorrectly) and applying various algorithms to find a function that minimizes this objective function is NP-hard. For example, fitting the weights of a neural network or finding the smallest decision tree are both NP-complete problems [Blum and Rivest, 1989; Quinlan and Rivest 1989]. Hence, heuristic algorithms such as gradient descent (for neural networks) and greedy search (for decision trees) have been applied with great success. Of course, the suboptimality of such heuristic algorithms ~mmediately suggests a reas&able line of research: find ~lgorithms that can search the hypothesis class better. Hence, there has been extensive research in applying secondorder methods to fit neural networks and in conducting much more thorough searches in learning decision trees and rule sets. Ironically, when these algorithms were tested on real datasets, it was found that their performance was often worse than simrde szradient descent or greedy sear~h [&inlan and Cameron-Jones 1995; Weigend 1994]. In short: it appears to be bet~er not to optimize! One of the other important trends in machine-learning research has been the establishment and nurturing of connections between various previously disparate fields, including computational learning theory, connectionist learning, symbolic learning. and statistics. The . connection to statistics was crucial in resolvins$ this naradox. The-key p~oblem arises from the structure of the machine-learning task, A learning algorithm is trained on a set of training data, but then it is applied to make predictions on new data points. The goal is to maximize its predictive accuracy on the new data points—not necessarily its accuracy on the trammg data. Indeed, if we work too hard to find the very best fit to the training data, there is a risk that we will fit the noise in the data by memorizing various peculiarities

Random Forests

Abstract: Random forests are a combination of tree predictors such that each tree depends on the values of a random vector sampled independently and with the same distribution for all trees in the forest. The generalization error for forests converges a.s. to a limit as the number of trees in the forest becomes large. The generalization error of a forest of tree classifiers depends on the strength of the individual trees in the forest and the correlation between them. Using a random selection of features to split each node yields error rates that compare favorably to Adaboost (Y. Freund & R. Schapire, Machine Learning: Proceedings of the Thirteenth International conference, aaa, 148–156), but are more robust with respect to noise. Internal estimates monitor error, strength, and correlation and these are used to show the response to increasing the number of features used in the splitting. Internal estimates are also used to measure variable importance. These ideas are also applicable to regression.

Gradient-based learning applied to document recognition

Abstract: Multilayer neural networks trained with the back-propagation algorithm constitute the best example of a successful gradient based learning technique. Given an appropriate network architecture, gradient-based learning algorithms can be used to synthesize a complex decision surface that can classify high-dimensional patterns, such as handwritten characters, with minimal preprocessing. This paper reviews various methods applied to handwritten character recognition and compares them on a standard handwritten digit recognition task. Convolutional neural networks, which are specifically designed to deal with the variability of 2D shapes, are shown to outperform all other techniques. Real-life document recognition systems are composed of multiple modules including field extraction, segmentation recognition, and language modeling. A new learning paradigm, called graph transformer networks (GTN), allows such multimodule systems to be trained globally using gradient-based methods so as to minimize an overall performance measure. Two systems for online handwriting recognition are described. Experiments demonstrate the advantage of global training, and the flexibility of graph transformer networks. A graph transformer network for reading a bank cheque is also described. It uses convolutional neural network character recognizers combined with global training techniques to provide record accuracy on business and personal cheques. It is deployed commercially and reads several million cheques per day.

Going deeper with convolutions

Abstract: We propose a deep convolutional neural network architecture codenamed Inception that achieves the new state of the art for classification and detection in the ImageNet Large-Scale Visual Recognition Challenge 2014 (ILSVRC14). The main hallmark of this architecture is the improved utilization of the computing resources inside the network. By a carefully crafted design, we increased the depth and width of the network while keeping the computational budget constant. To optimize quality, the architectural decisions were based on the Hebbian principle and the intuition of multi-scale processing. One particular incarnation used in our submission for ILSVRC14 is called GoogLeNet, a 22 layers deep network, the quality of which is assessed in the context of classification and detection.

Deep Learning

Abstract: Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning. The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models. Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.

Reinforcement Learning: An Introduction

Abstract: Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives when interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the key ideas and algorithms of reinforcement learning. Their discussion ranges from the history of the field's intellectual foundations to the most recent developments and applications. The only necessary mathematical background is familiarity with elementary concepts of probability. The book is divided into three parts. Part I defines the reinforcement learning problem in terms of Markov decision processes. Part II provides basic solution methods: dynamic programming, Monte Carlo methods, and temporal-difference learning. Part III presents a unified view of the solution methods and incorporates artificial neural networks, eligibility traces, and planning; the two final chapters present case studies and consider the future of reinforcement learning.