Given a collection of records (training set )
Each record contains a set of attributes, one of the attributes is the class.
Find a model for class attribute as a function of the values of other attributes.
Goal: previously unseen records should be assigned a class as accurately as possible.
A test set is used to determine the accuracy of the model. Usually, the given data set is divided into training and test sets, with training set used to build the model and test set used to validate it.
Morphological segmentation has been shown to be beneﬁcial to a range of NLP tasks such as machine translation, speech recognition, speech synthesis and information retrieval. Recently, a number of approaches to unsupervised morphological segmentation have been proposed. This paper describes an algorithm that draws from previous approaches and combines them into a simple model for morphological segmentation that outperforms other approaches on English and German, and also yields good results on agglutinative languages such as Finnish and Turkish. ...
It is well known that occurrence counts of words in documents are often modeled poorly by standard distributions like the binomial or Poisson. Observed counts vary more than simple models predict, prompting the use of overdispersed models like Gamma-Poisson or Beta-binomial mixtures as robust alternatives. Another deﬁciency of standard models is due to the fact that most words never occur in a given document, resulting in large amounts of zero counts. We propose using zeroinﬂated models for dealing with this, and evaluate competing models on a Naive Bayes text classiﬁcation task.
Tuyển tập các báo cáo nghiên cứu về y học được đăng trên tạp chí y học quốc tế cung cấp cho các bạn kiến thức về ngành y đề tài: Research Chemotherapy in conjoint aging-tumor systems: some simple models for addressing coupled aging-cancer dynamics...
We propose a new, simple model for the automatic induction of selectional preferences, using corpus-based semantic similarity metrics. Focusing on the task of semantic role labeling, we compute selectional preferences for semantic roles. In evaluations the similarity-based model shows lower error rates than both Resnik’s WordNet-based model and the EM-based clustering model, but has coverage problems.
Data Streams: Models and Algorithms primarily discusses issues related to the mining aspects of data streams. Recent progress in hardware technology makes it possible for organizations to store and record large streams of transactional data. For example, even simple daily transactions such as using the credit card or phone result in automated data storage, which brings us to a fairly new topic called data streams.
Beginning Blender covers the Blender 2.5 release in-depth. The book starts with the creation of simple figures using basic modeling and sculpting. It then teaches you how to bridge from modeling to animation, and from scene setup to texture creation and rendering, lighting, rigging, and ultimately, full animation. You will create and mix your own movie scenes, and you will even learn the basics of games logic and how to deal with games physics.
The estimation process begins by assuming or hypothesizing that the least squares linear regression
model (drawn from a sample) is valid. The formal two-variable linear regression model is based on
the following assumptions:
(1) The population regression is adequately represented by a straight line: E(Yi) = μ(Xi) = β0 + β1Xi
(2) The error terms have zero mean: E(∈i) = 0
(3) A constant variance (homoscedasticity): V(∈i) = σ2
TWO ESSAYS IN INTERNATIONAL ECONOMICS: AN EMPIRICAL APPROACH TO PURCHASING POWER PARITY AND THE MONETARY MODEL OF EXCHANGE RATE DETERMINATION I adopt a different strategy: I compare housing markets that differ in the strength of
the residential location-school assignment link, and I develop simple reduced-form
implications of parental valuations for the across-school distribution of student
characteristics and educational outcomes as a function of the strength of this link.
The book deals with the MOS Field Effect Transistor (MOSFET) models that are derived from basic semiconductor theory. Various models are developed, ranging from simple to more sophisticated models that take into account new physical effects observed in submicron transistors used in today's (1993) MOS VLSI technology. The assumptions used to arrive at the models are emphasized so that the accuracy of the models in describing the device characteristics are clearly understood. Due to the importance of designing reliable circuits, device reliability models are also covered.
We describe a simple variant of the interpolated Markov model with non-emitting state transitions and prove that it is strictly more powerful than any Markov model. Empirical results demonstrate that the non-emitting model outperforms the interpolated model on the Brown corpus and on the Wall Street Journal under a wide range of experimental conditions. The nonemitting model is also much less prone to overtraining. The remainder of our article consists of four sections.
The take-oﬀ manoeuvre of a vehicle was studied in Section 23.9 using a simple model where the inertia of both engine and vehicle were modelled as two ﬂywheels connected to each other by a rigid shaft and a friction clutch. This model can be made more realistic by adding the torsional compliance of the shaft, of the joints and possibly the gear wheels, as well as the rotational inertia of the various elements of the driveline. A model of the whole driveline is thus obtained, with the engine and vehicle modelled as two ﬂywheels located at its ends. However, the...
What makes populations stabilize? What makes them fluctuate? Are populations in complex ecosystems more stable than populations in simple ecosystems? In 1973, Robert May addressed these questions in this classic book. May investigated the mathematical roots of population dynamics and argued-counter to most current biological thinking-that complex ecosystems in themselves do not lead to population stability.
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 statistical model of Japanese unknown words consisting of a set of length and spelling models classified by the character types that constitute a word. The point is quite simple: different character sets should be treated differently and the changes between character types are very important because Japanese script has both ideograms like Chinese (kanji) and phonograms like English (katakana). Both word segmentation accuracy and part of speech tagging accuracy are improved by the proposed model. ...
This innovative text presents computer programming as a unified discipline in a way that is both practical and scientifically sound. The book focuses on techniques of lasting value and explains them precisely in terms of a simple abstract machine. The book presents all major programming paradigms in a uniform framework that shows their deep relationships and how and where to use them together.After an introduction to programming concepts, the book presents both well-known and lesser-known computation models ("programming paradigms"). ...
Analyzing future distributed real-time systems, automotive
and avionic systems, is requiring compositional hard
real-time analysis techniques. Well known established techniques
as SymTA/S and the real-time calculus are candidates solving
the mentioned problem. However both techniques use quite
simple event models. SymTA/S is based on discrete events the
real-time calculus on continuous functions. Such simple models
has been choosen because of the computational complexity of
the considered mathematical operations required for real-time
The applicability of many current information extraction techniques is severely limited by the need for supervised training data. We demonstrate that for certain ﬁeld structured extraction tasks, such as classiﬁed advertisements and bibliographic citations, small amounts of prior knowledge can be used to learn effective models in a primarily unsupervised fashion. Although hidden Markov models (HMMs) provide a suitable generative model for ﬁeld structured text, general unsupervised HMM learning fails to learn useful structure in either of our domains.
We investigate a number of simple methods for improving the word-alignment accuracy of IBM Model 1. We demonstrate reduction in alignment error rate of approximately 30% resulting from (1) giving extra weight to the probability of alignment to the null word, (2) smoothing probability estimates for rare words, and (3) using a simple heuristic estimation method to initialize, or replace, EM training of model parameters.