عنوان مقاله | نویسنده(ها) | مربوط به کنفرانس | چکیده | خرید مقاله |
---|---|---|---|---|
Atefeh Torkaman, Nasrollah Moghaddam Charkari, Mahnaz Aghaeipour
|
چهاردهمین کنفرانس بینالمللی سالانه انجمن کامپیوتر ایران
|
Classification is a well known task in data mining
and machine learning that aims to predict the class of
items as accurately as possible. A well planned data
classification system makes essential data easy to find.
An object is classified into one of the categories called
classes according to the features that well separated
the classes. Actually, classification maps an object to
its classification label. Many researches used different
learning algorithms to classify data; neural networks,
decision trees, etc.
In this paper, a new classification approach based
on cooperative game is proposed. Cooperative game is
a branch of game theory consists of a set of players
and a characteristic function which specifies the value
created by different subsets of the players in the game.
In order to find classes in classification process,
objects can be imagine as the players in a game and
according to the values which obtained by these
players, classes will be separated. This approach can
be used to classify a population according to their
contributions. In the other words, it applies equally to
different types of data. Through out this paper, a
special case in medical diagnosis was studied. 304
samples taken from human leukemia tissue consists of
17 attributes which determine different CD markers
related to leukemia were analyzed. These samples
collected from different types of leukemia at Iran Blood
Transfusion Organization (IBTO). Obtained results
demonstrate that cooperative game is very promising
to use directly for classification.
|
||
S.A. Hosseini Amereii, M.M. Homayounpour
|
چهاردهمین کنفرانس بینالمللی سالانه انجمن کامپیوتر ایران
|
Two popular and better performing approaches to
language Identification (LID) are Phone Recognition
followed by Language Modeling (PRLM) and Parallel
PRLM. In this paper, we report several improvements
in Phone Recognition which reduces error rate in
PRLM and PPRLM based LID systems. In our previous
paper, we introduced APRLM approach that reduces
error rate for about 1.3% in LID tasks. In this paper,
we suggest other solution that overcomes APRLM. This
new LID approach is named Generalized PRLM or
GPRLM. Several language identification experiments
were conducted and the proposed improvements were
evaluated using OGI-MLTS corpus. Our results show
that GPRLM overcomes PPRLM and APRLM about
2.5% and 1.2% respectively in two language
classification tasks.
|
||
A. Mehdi, R. R. Ggholam-ali
|
چهاردهمین کنفرانس بینالمللی سالانه انجمن کامپیوتر ایران
|
This paper estimates and segments the moving objects
based on center of mass model to decrease the search window
and to provide a new algorithm, which achieves an accurate
and rapid tracking.
Furthermore, a novel method is proposed to update the
template size adaptively by using estimation and segmentation
of moving objects. The estimated results of moving target is
transformed to wavelet domain and target tracking is
performed in that domain. To improve the algorithm center of
mass model is performed in wavelet domain. By using kalman
predictor and thresholding method, a new approach is
presented for object tracking failure and recovery.
|
||
Rezvan Kianifar, Farzad Towhidkhah
|
چهاردهمین کنفرانس بینالمللی سالانه انجمن کامپیوتر ایران
|
Human can determine optimal behaviors which
depend on the ability to make planned and adaptive
decisions. In this paper, we have proposed a predictive
structure based on neuropsychological evidences to
model human decision making process by
concentrating on the role of frontal brain regions
which are responsible for predictive control of human
behavior. We have considered a model-based
reinforcement learning framework to implement the
relations between these brain areas. Finally, we have
designed an experimental test to compare the function
of model with human behavior in a maze task. Our
results reveal that there is more than reward and
punishment in human behavior, and considering
higher cognitive functions such as prediction will help
to have more reliable models which could better
describe human behavior.
|
||
زهرا افصحی
|
پانزدهمین کنفرانس ملی سالانه انجمن کامپیوتر ایران
|
|
||
مهدی اکرمی, محمدرضا رزازی
|
پانزدهمین کنفرانس ملی سالانه انجمن کامپیوتر ایران
|
|
||
محمد طاهر پیلهور, هشام فیلی
|
پانزدهمین کنفرانس ملی سالانه انجمن کامپیوتر ایران
|
|
||
نستوه طاهری جوان, آرش نصیری اقبالی
|
پانزدهمین کنفرانس ملی سالانه انجمن کامپیوتر ایران
|
|
||
فهیمه فتاحپور, خشایار یغمایی
|
پانزدهمین کنفرانس ملی سالانه انجمن کامپیوتر ایران
|
|
||
مریم سنقرزاده, راهبه نیارکی اصل
|
پانزدهمین کنفرانس ملی سالانه انجمن کامپیوتر ایران
|
|
||
حوا علیزاده نوقابی, فرزانه غیور باغبانی
|
پانزدهمین کنفرانس ملی سالانه انجمن کامپیوتر ایران
|
|
||
سیدمحمد بیدکی, محمد هادی صدرالدینی, منصور ذوالقدری, نادعلی محمودی کهن
|
پانزدهمین کنفرانس ملی سالانه انجمن کامپیوتر ایران
|
|
||
جواد محبی نجمآباد, هادی آدینه, حسین دلداری
|
پانزدهمین کنفرانس ملی سالانه انجمن کامپیوتر ایران
|
|
||
اباذر برزگر, مصطفی جهانگیر, محمد حسن بیات
|
پانزدهمین کنفرانس ملی سالانه انجمن کامپیوتر ایران
|
|
||
مسعود بشیری, سعید شیری قیداری
|
پانزدهمین کنفرانس ملی سالانه انجمن کامپیوتر ایران
|
|
||
محمد حسینزاده مقدم, علیرضا باقری, علی صفری ممقانی
|
پانزدهمین کنفرانس ملی سالانه انجمن کامپیوتر ایران
|
|
||
سعید احمدی ارزیل, محمد علی جبرئیل جمالی, مصطفی حقیفام
|
پانزدهمین کنفرانس ملی سالانه انجمن کامپیوتر ایران
|
|
||
اسماء شمسی, حسین نظامآبادیپور, سعید سریزدی, احساناله کبیر
|
پانزدهمین کنفرانس ملی سالانه انجمن کامپیوتر ایران
|
|
||
معصومه صادقی, نسرین دسترنج ممقانی, فریبرز موسوی
|
پانزدهمین کنفرانس ملی سالانه انجمن کامپیوتر ایران
|
|
||
احسان وجدانی محمودی, فاطمه سعادتمند, مسعود نیازی ترشیز, قمرناز تدین تبریزی
|
پانزدهمین کنفرانس ملی سالانه انجمن کامپیوتر ایران
|
|