21,442 research outputs found
Committee-Based Sample Selection for Probabilistic Classifiers
In many real-world learning tasks, it is expensive to acquire a sufficient
number of labeled examples for training. This paper investigates methods for
reducing annotation cost by `sample selection'. In this approach, during
training the learning program examines many unlabeled examples and selects for
labeling only those that are most informative at each stage. This avoids
redundantly labeling examples that contribute little new information. Our work
follows on previous research on Query By Committee, extending the
committee-based paradigm to the context of probabilistic classification. We
describe a family of empirical methods for committee-based sample selection in
probabilistic classification models, which evaluate the informativeness of an
example by measuring the degree of disagreement between several model variants.
These variants (the committee) are drawn randomly from a probability
distribution conditioned by the training set labeled so far. The method was
applied to the real-world natural language processing task of stochastic
part-of-speech tagging. We find that all variants of the method achieve a
significant reduction in annotation cost, although their computational
efficiency differs. In particular, the simplest variant, a two member committee
with no parameters to tune, gives excellent results. We also show that sample
selection yields a significant reduction in the size of the model used by the
tagger
Fast Conical Hull Algorithms for Near-separable Non-negative Matrix Factorization
The separability assumption (Donoho & Stodden, 2003; Arora et al., 2012)
turns non-negative matrix factorization (NMF) into a tractable problem.
Recently, a new class of provably-correct NMF algorithms have emerged under
this assumption. In this paper, we reformulate the separable NMF problem as
that of finding the extreme rays of the conical hull of a finite set of
vectors. From this geometric perspective, we derive new separable NMF
algorithms that are highly scalable and empirically noise robust, and have
several other favorable properties in relation to existing methods. A parallel
implementation of our algorithm demonstrates high scalability on shared- and
distributed-memory machines.Comment: 15 pages, 6 figure
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
From Frequency to Meaning: Vector Space Models of Semantics
Computers understand very little of the meaning of human language. This
profoundly limits our ability to give instructions to computers, the ability of
computers to explain their actions to us, and the ability of computers to
analyse and process text. Vector space models (VSMs) of semantics are beginning
to address these limits. This paper surveys the use of VSMs for semantic
processing of text. We organize the literature on VSMs according to the
structure of the matrix in a VSM. There are currently three broad classes of
VSMs, based on term-document, word-context, and pair-pattern matrices, yielding
three classes of applications. We survey a broad range of applications in these
three categories and we take a detailed look at a specific open source project
in each category. Our goal in this survey is to show the breadth of
applications of VSMs for semantics, to provide a new perspective on VSMs for
those who are already familiar with the area, and to provide pointers into the
literature for those who are less familiar with the field
A New Feature Selection Method based on Intuitionistic Fuzzy Entropy to Categorize Text Documents
Selection of highly discriminative feature in text document plays a major challenging role in categorization. Feature selection is an important task that involves dimensionality reduction of feature matrix, which in turn enhances the performance of categorization. This article presents a new feature selection method based on Intuitionistic Fuzzy Entropy (IFE) for Text Categorization. Firstly, Intuitionistic Fuzzy C-Means (IFCM) clustering method is employed to compute the intuitionistic membership values. The computed intuitionistic membership values are used to estimate intuitionistic fuzzy entropy via Match degree. Further, features with lower entropy values are selected to categorize the text documents. To find the efficacy of the proposed method, experiments are conducted on three standard benchmark datasets using three classifiers. F-measure is used to assess the performance of the classifiers. The proposed method shows impressive results as compared to other well known feature selection methods. Moreover, Intuitionistic Fuzzy Set (IFS) property addresses the uncertainty limitations of traditional fuzzy set
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