There is a bustling atmosphere in the headquarters of the globally active Confusio
Corporation. Everything seems to be just fine. Yet, there is a bad atmosphere in
the precious wood-paneled conference room of the managing director Paul Peppy.
Peppy has drummed together his top managers from all important branch offices; a
hard and uncompromising crackdown is urgently required! Concerning the topic of
the crisis summit, he has intentionally left the participants in the dark.
This emphasis on the spectacular and exotic, consistent with an oppositional or
marginal view of the consumer, is often also emphasised in academic writing on
counterfeiting. Consumers of counterfeits are often represented through anecdotal
narratives which serve as a proxy for deeper understanding of consumer motivations.
For example, Lasica (2005) illustrates his work with case studies which potentially
confuse everyday users with vanguard consumers.
This work investigates supervised word alignment methods that exploit inversion transduction grammar (ITG) constraints. We consider maximum margin and conditional likelihood objectives, including the presentation of a new normal form grammar for canonicalizing derivations. Even for non-ITG sentence pairs, we show that it is possible learn ITG alignment models by simple relaxations of structured discriminative learning objectives. For efﬁciency, we describe a set of pruning techniques that together allow us to align sentences two orders of magnitude faster than naive bitext CKY parsing.
We present a probabilistic topic model for jointly identifying properties and attributes of social media review snippets. Our model simultaneously learns a set of properties of a product and captures aggregate user sentiments towards these properties. This approach directly enables discovery of highly rated or inconsistent properties of a product. Our model admits an efﬁcient variational meanﬁeld inference algorithm which can be parallelized and run on large snippet collections.
We consider a new subproblem of unsupervised parsing from raw text, unsupervised partial parsing—the unsupervised version of text chunking. We show that addressing this task directly, using probabilistic ﬁnite-state methods, produces better results than relying on the local predictions of a current best unsupervised parser, Seginer’s (2007) CCL. These ﬁnite-state models are combined in a cascade to produce more general (full-sentence) constituent structures; doing so outperforms CCL by a wide margin in unlabeled PARSEVAL scores for English, German and Chinese. ...
We investigate systems that identify opinion expressions and assigns polarities to the extracted expressions. In particular, we demonstrate the beneﬁt of integrating opinion extraction and polarity classiﬁcation into a joint model using features reﬂecting the global polarity structure. The model is trained using large-margin structured prediction methods.
In this paper, we ﬁrst demonstrate the interest of the Loopy Belief Propagation algorithm to train and use a simple alignment model where the expected marginal values needed for an efﬁcient EM-training are not easily computable. We then improve this model with a distortion model based on structure conservation.
In this paper, we present a supervised learning approach to training submodular scoring functions for extractive multidocument summarization. By taking a structured prediction approach, we provide a large-margin method that directly optimizes a convex relaxation of the desired performance measure.
This chapter introduces the analysis of managerial decision making that will occupy us for the remainder of the book. The chapter is devoted to two man topics. The first is a simple economic model (i.e., a description) of the private, profit-maximizing firm. The second is an introduction to marginal analysis, an important tool for arriving at optimal decisions. Indeed, it is fair to say that the subsequent chapters provide extensions or variations on these two themes
We address the problem of learning the mapping between words and their possible pronunciations in terms of sub-word units. Most previous approaches have involved generative modeling of the distribution of pronunciations, usually trained to maximize likelihood. We propose a discriminative, feature-rich approach using large-margin learning. This approach allows us to optimize an objective closely related to a discriminative task, to incorporate a large number of complex features, and still do inference efﬁciently. ...
This book grows out of 20 years’ banking research and training of bankers in
Europe, the Americas, Africa and Asia. As deregulation and competition are
reducing margins around the world, the need for knowledge on Asset and
Liability Management, the control of bank’s profit and risks, becomes an
absolute necessity for any banker in charge of a profit centre, central bankers
in charge of bank supervision, and banks’ auditors, consultants or lawyers.
Whether you work for a Fortune 500 company, a small company, a government agency, or a non-profit organization, if you're reading this introduction is that you can use Microsoft Excel at work your daily. Your job may be related to the synthesis, reporting, and analysis of data. It may also be related to the construction of analytical models to help employers tangloi your margins, reduce costs, manage operations more efficiently.
Trong CSS, box model (mô hình hộp) mô tả cách mà CSS định dạng khối không gian bao quanh một thành phần. Nó bao gồm padding (vùng đệm), border (viền) và margin (canh lề) và các tùy chọn. Hình bên dưới mô tả cấu trúc minh họa mô hình hộp cho một thành phần web.
Trong CSS, box model (mô hình hộp) mô tả cách mà CSS định dạng khối không gian bao quanh một thành phần.
Nowadays, huge amount of multimedia data are being constantly generated in
various forms from various places around the world. With ever increasing complexity
and variability of multimedia data, traditional rule-based approaches
where humans have to discover the domain knowledge and encode it into a
set of programming rules are too costly and incompetent for analyzing the
contents, and gaining the intelligence of this glut of multimedia data.
The challenges in data complexity and variability have led to revolutions
in machine learning techniques.
In the European Union, the existing insurance and reinsurance directives do not contain any
provisions that place reliance on credit rating agencies. There is no explicit credit risk charge
for the solvency margin in the Solvency I framework. The solvency margin in the Solvency I
framework is not the sum of different capital charges related to different risks, but a single
capital charge calibrated to reflect all the risks an insurance company faces.
In the next section we outline some theoretical and empirical results about
the relationships between monetary policy, the ináation target of monetary
authorities, the level of this target perceived by the public and the long term
interest rates dynamics. In section 3, we present the works of Kozicki and
Tinsley (1998, 2001a, 2001b) and we establish the interest of this model
in our framework.
Tuyển tập các báo cáo nghiên cứu về sinh học được đăng trên tạp chí sinh Original cung cấp cho các bạn kiến thức về sinh học đề tài:Phenotypic plasticity of body pigmentation in Drosophila: Computing marginal posterior densities of genetic parameters of a multiple trait animal model using Laplace approximation or Gibbs sampling...
An investor who has sold stock short in anticipation of a price decline can limit a
possible loss by purchasing call options. Remember that shorting stock requires a
margin account and margin calls may force you to liquidate your position prematurely.
Although a call option may be used to offset a short stock position's upside risk, it does
not protect the option holder against additional margin calls or premature liquidation
of the short stock position.
Assume you sold short 100 shares of XYZ stock at $40 per share.