583 research outputs found
Mistake-Driven Learning in Text Categorization
Learning problems in the text processing domain often map the text to a space
whose dimensions are the measured features of the text, e.g., its words. Three
characteristic properties of this domain are (a) very high dimensionality, (b)
both the learned concepts and the instances reside very sparsely in the feature
space, and (c) a high variation in the number of active features in an
instance. In this work we study three mistake-driven learning algorithms for a
typical task of this nature -- text categorization. We argue that these
algorithms -- which categorize documents by learning a linear separator in the
feature space -- have a few properties that make them ideal for this domain. We
then show that a quantum leap in performance is achieved when we further modify
the algorithms to better address some of the specific characteristics of the
domain. In particular, we demonstrate (1) how variation in document length can
be tolerated by either normalizing feature weights or by using negative
weights, (2) the positive effect of applying a threshold range in training, (3)
alternatives in considering feature frequency, and (4) the benefits of
discarding features while training. Overall, we present an algorithm, a
variation of Littlestone's Winnow, which performs significantly better than any
other algorithm tested on this task using a similar feature set.Comment: 9 pages, uses aclap.st
Numerical analysis of least squares and perceptron learning for classification problems
This work presents study on regularized and non-regularized versions of
perceptron learning and least squares algorithms for classification problems.
Fr'echet derivatives for regularized least squares and perceptron learning
algorithms are derived. Different Tikhonov's regularization techniques for
choosing the regularization parameter are discussed. Decision boundaries
obtained by non-regularized algorithms to classify simulated and experimental
data sets are analyzed
Using online linear classifiers to filter spam Emails
The performance of two online linear classifiers - the Perceptron and Littlestoneās Winnow ā is explored for two anti-spam filtering benchmark corpora - PU1 and Ling-Spam. We study the performance for varying numbers of features, along with three different feature selection methods: Information Gain (IG), Document Frequency (DF) and Odds Ratio. The size of the training set and the number of training iterations are also investigated for both classifiers. The experimental results show that both the Perceptron and Winnow perform much better when using IG or DF than using Odds Ratio. It is further demonstrated that when using IG or DF, the classifiers are insensitive to the number of features and the number of training iterations, and not greatly sensitive to the size of training set. Winnow is shown to slightly outperform the Perceptron. It is also demonstrated that both of these online classifiers perform much better than a standard NaĆÆve Bayes method. The theoretical and implementation computational complexity of these two classifiers are very low, and they are very easily adaptively updated. They outperform most of the published results, while being significantly easier to train and adapt. The analysis and promising experimental results indicate that the Perceptron and Winnow are two very competitive classifiers for anti-spam filtering
Learning to Resolve Natural Language Ambiguities: A Unified Approach
We analyze a few of the commonly used statistics based and machine learning
algorithms for natural language disambiguation tasks and observe that they can
be re-cast as learning linear separators in the feature space. Each of the
methods makes a priori assumptions, which it employs, given the data, when
searching for its hypothesis. Nevertheless, as we show, it searches a space
that is as rich as the space of all linear separators. We use this to build an
argument for a data driven approach which merely searches for a good linear
separator in the feature space, without further assumptions on the domain or a
specific problem.
We present such an approach - a sparse network of linear separators,
utilizing the Winnow learning algorithm - and show how to use it in a variety
of ambiguity resolution problems. The learning approach presented is
attribute-efficient and, therefore, appropriate for domains having very large
number of attributes.
In particular, we present an extensive experimental comparison of our
approach with other methods on several well studied lexical disambiguation
tasks such as context-sensitive spelling correction, prepositional phrase
attachment and part of speech tagging. In all cases we show that our approach
either outperforms other methods tried for these tasks or performs comparably
to the best
Machine Learning in Automated Text Categorization
The automated categorization (or classification) of texts into predefined
categories has witnessed a booming interest in the last ten years, due to the
increased availability of documents in digital form and the ensuing need to
organize them. In the research community the dominant approach to this problem
is based on machine learning techniques: a general inductive process
automatically builds a classifier by learning, from a set of preclassified
documents, the characteristics of the categories. The advantages of this
approach over the knowledge engineering approach (consisting in the manual
definition of a classifier by domain experts) are a very good effectiveness,
considerable savings in terms of expert manpower, and straightforward
portability to different domains. This survey discusses the main approaches to
text categorization that fall within the machine learning paradigm. We will
discuss in detail issues pertaining to three different problems, namely
document representation, classifier construction, and classifier evaluation.Comment: Accepted for publication on ACM Computing Survey
- ā¦