In machine learning, whether one can build a more accurate classiﬁer by using unlabeled data (semi-supervised learning) is an important issue. Although a number of semi-supervised methods have been proposed, their effectiveness on NLP tasks is not always clear. This paper presents a novel semi-supervised method that employs a learning paradigm which we call structural learning.
We investigate a family of update methods for online machine learning algorithms for cost-sensitive multiclass and structured classiﬁcation problems. The update rules are based on multinomial logistic models. The most interesting question for such an approach is how to integrate the cost function into the learning paradigm. We propose a number of solutions to this problem. To demonstrate the applicability of the algorithms, we evaluated them on a number of classiﬁcation tasks related to incremental dependency parsing. ...
We analyze collective discourse, a collective human behavior in content generation, and show that it exhibits diversity, a property of general collective systems. Using extensive analysis, we propose a novel paradigm for designing summary generation systems that reﬂect the diversity of perspectives seen in reallife collective summarization. We analyze 50 sets of summaries written by human about the same story or artifact and investigate the diversity of perspectives across these summaries.
Most previous work on trainable language generation has focused on two paradigms: (a) using a statistical model to rank a set of generated utterances, or (b) using statistics to inform the generation decision process. Both approaches rely on the existence of a handcrafted generator, which limits their scalability to new domains. This paper presents BAGEL, a statistical language generator which uses dynamic Bayesian networks to learn from semantically-aligned data produced by 42 untrained annotators. ...
Translation needs have greatly increased during the last years. In many situations, text to be translated constitutes an unbounded stream of data that grows continually with time. An effective approach to translate text documents is to follow an interactive-predictive paradigm in which both the system is guided by the user and the user is assisted by the system to generate error-free translations. Unfortunately, when processing such unbounded data streams even this approach requires an overwhelming amount of manpower. ...
We propose a novel method for learning morphological paradigms that are structured within a hierarchy. The hierarchical structuring of paradigms groups morphologically similar words close to each other in a tree structure. This allows detecting morphological similarities easily leading to improved morphological segmentation. Our evaluation using (Kurimo et al., 2011a; Kurimo et al., 2011b) dataset shows that our method performs competitively when compared with current state-ofart systems.
This paper analyses the relation between the use of similarity in Memory-Based Learning and the notion of backed-off smoothing in statistical language modeling. We show that the two approaches are closely related, and we argue that feature weighting methods in the Memory-Based paradigm can offer the advantage of automatically specifying a suitable domainspecific hierarchy between most specific and most general conditioning information without the need for a large number of parameters. We report two applications of this approach: PP-attachment and POStagging. ...
We extend the classical single-task active learning (AL) approach. In the multi-task active learning (MTAL) paradigm, we select examples for several annotation tasks rather than for a single one as usually done in the context of AL. We introduce two MTAL metaprotocols, alternating selection and rank combination, and propose a method to implement them in practice. We experiment with a twotask annotation scenario that includes named entity and syntactic parse tree annotations on three different corpora. ...
Chapter 1: Overview on Pattern Recognition and Machine Learning includes about Pattern Recognition, Machine learning, Related fields of pattern recognition, Classification, Two paradigms of pattern recognition.
restrictions. AR also doesn't consume as much time and effort in many applications
because it's not required to construct the entire virtual scene, which can be tedious and
In this book, several new and emerging application areas of AR are presented. It is
divided into three sections. The first section contains applications in outdoor and
mobile AR, such as construction, restoration, security, and surveillance. The second
section deals with AR in medical, biological, and human bodies.
Even since computers were invented many decades ago, many researchers have been
trying to understand how human beings learn and many interesting paradigms and
approaches towards emulating human learning abilities have been proposed. The ability of
learning is one of the central features of human intelligence, which makes it an important
ingredient in both traditional Artificial Intelligence (AI) and emerging Cognitive Science.
Man has learned much from studies of natural systems, using what has been learned
to develop new algorithmic models to solve complex problems. This book presents an
introduction to some of these technological paradigms, under the umbrella of compu-
tational intelligence (CI).
The prevailing low food production in sub-Saharan Africa is an issue of
great concern especially since Africa south of the Sahara is the only remaining
region of the world where per capita food production has remained stagnant. This
chapter reviews long-term experiments in Africa in the context of shifting paradigms
related to tropical soil fertility management from fi rst external input paradigm right
through to the current Integrated Soil Fertility Management (ISFM) approach,
which is a culmination of the participatory methods developed along the paradigm
Today, space technology is used as an excellent instrument for Earth observation applications. Data is collected using satellites and other available platforms for remote sensing. Remote sensing data collection detects a wide range of electromagnetic energy which is emitting, transmitting, or reflecting from the Earth's surface. Appropriate detection systems are needed to implement further data processing. Space technology has been found to be a successful application for studying climate change, as current and past data can be dynamically compared.
Brains rule the world, and brain-like computation is increasingly used in computers and electronic
devices. Brain-like computation is about processing and interpreting data or directly
putting forward and performing actions. Learning is a very important aspect. This book is
on reinforcement learning which involves performing actions to achieve a goal. Two other
learning paradigms exist. Supervised learning has initially been successful in prediction and
classification tasks, but is not brain-like....
This book describes the object-oriented (OO) paradigm, a development
strategy based on the concept that systems should be built from a collection
of reusable components called objects. Instead of separating data and
functionality, as is done in the structured paradigm, objects encompass
both. While the object-oriented paradigm sounds similar to the structured
paradigm, as you will see in this book, it is actually quite different.
If one posits a powerful connection among learning, doing and understanding,
then the argument for teaching media literacy is a compelling one. And equally, there
is a compelling case to be made that the teaching of media literacy should be global –
the content of the course should be global, because the effects of media messages
There is no global issue or political arena in which the statement of problems
and the framing of possible solutions are not influenced by media coverage.
Product designers learned years ago that
they’d save time and money if they consulted
with their colleagues in manufacturing rather
than just throwing new designs over the wall.
The two functions realized it wasn’t enough to
just coexist—not when they could work together
to create value for the company and for
customers. You’d think that marketing and
sales teams, whose work is also deeply interconnected,
would have discovered something
As Thomas writes, media education in India is still in an experimental stage
with very little feedback. Besides the concepts of media education are rather geared
to the Western hemisphere and India being a developing country has very different
concerns about development. These kinds of changes in the Asian context demand
an alternative definition and approach to media education to the one outlined by
Chapter 18 - Managing innovation and change. In this chapter, the learning objectives are: Explain how paradigm shifts occur and describe their consequences, identify the major sources of organizational inertia, outline what is required to change the strategy and organization of an established enterprise,...