5 research outputs found
Can Boosting with SVM as Week Learners Help?
Object recognition in images involves identifying objects with partial
occlusions, viewpoint changes, varying illumination, cluttered backgrounds.
Recent work in object recognition uses machine learning techniques SVM-KNN,
Local Ensemble Kernel Learning, Multiple Kernel Learning. In this paper, we
want to utilize SVM as week learners in AdaBoost. Experiments are done with
classifiers like near- est neighbor, k-nearest neighbor, Support vector
machines, Local learning(SVM- KNN) and AdaBoost. Models use Scale-Invariant
descriptors and Pyramid his- togram of gradient descriptors. AdaBoost is
trained with set of week classifier as SVMs, each with kernel distance function
on different descriptors. Results shows AdaBoost with SVM outperform other
methods for Object Categorization dataset.Comment: Work done in 200
System for Filtering Messages on Social Media Content
The social networking era has left us with little privacy. The details of the
social network users are published on Social Networking sites. Vulnerability
has reached new heights due to the overpowering effects of social networking.
The sites like Facebook, Twitter are having a huge set of users who publish
their files, comments, messages in other users walls. These messages and
comments could be of any nature. Even friends could post a comment that would
harm a persons integrity. Thus there has to be a system which will monitor the
messages and comments that are posted on the walls. If the messages are found
to be neutral (does not have any harmful content), then it can be published. If
the messages are found to have non-neutral content in them, then these messages
would be blocked by the social network manager. The messages that are
non-neutral would be of sexual, offensive, hatred, pun intended nature. Thus
the social network manager can classify content as neutral and non-neutral and
notify the user if there seems to be messages of non-neutral behavior
Finding Optimal Combination of Kernels using Genetic Programming
In Computer Vision, problem of identifying or classifying the objects present
in an image is called Object Categorization. It is a challenging problem,
especially when the images have clutter background, occlusions or different
lighting conditions. Many vision features have been proposed which aid object
categorization even in such adverse conditions. Past research has shown that,
employing multiple features rather than any single features leads to better
recognition. Multiple Kernel Learning (MKL) framework has been developed for
learning an optimal combination of features for object categorization. Existing
MKL methods use linear combination of base kernels which may not be optimal for
object categorization. Real-world object categorization may need to consider
complex combination of kernels(non-linear) and not only linear combination.
Evolving non-linear functions of base kernels using Genetic Programming is
proposed in this report. Experiment results show that non-kernel generated
using genetic programming gives good accuracy as compared to linear combination
of kernels
Accessing accurate documents by mining auxiliary document information
Earlier techniques of text mining included algorithms like k-means, Naive
Bayes, SVM which classify and cluster the text document for mining relevant
information about the documents. The need for improving the mining techniques
has us searching for techniques using the available algorithms. This paper
proposes one technique which uses the auxiliary information that is present
inside the text documents to improve the mining. This auxiliary information can
be a description to the content. This information can be either useful or
completely useless for mining. The user should assess the worth of the
auxiliary information before considering this technique for text mining. In
this paper, a combination of classical clustering algorithms is used to mine
the datasets. The algorithm runs in two stages which carry out mining at
different levels of abstraction. The clustered documents would then be
classified based on the necessary groups. The proposed technique is aimed at
improved results of document clustering
Thesis: Multiple Kernel Learning for Object Categorization
Object Categorization is a challenging problem, especially when the images
have clutter background, occlusions or different lighting conditions. In the
past, many descriptors have been proposed which aid object categorization even
in such adverse conditions. Each descriptor has its own merits and de-merits.
Some descriptors are invariant to transformations while the others are more
discriminative. Past research has shown that, employing multiple descriptors
rather than any single descriptor leads to better recognition. The problem of
learning the optimal combination of the available descriptors for a particular
classification task is studied. Multiple Kernel Learning (MKL) framework has
been developed for learning an optimal combination of descriptors for object
categorization. Existing MKL formulations often employ block l-1 norm
regularization which is equivalent to selecting a single kernel from a library
of kernels. Since essentially a single descriptor is selected, the existing
formulations maybe sub- optimal for object categorization. A MKL formulation
based on block l-infinity norm regularization has been developed, which chooses
an optimal combination of kernels as opposed to selecting a single kernel. A
Composite Multiple Kernel Learning(CKL) formulation based on mixed l-infinity
and l-1 norm regularization has been developed. These formulations end in
Second Order Cone Programs(SOCP). Other efficient alter- native algorithms for
these formulation have been implemented. Empirical results on benchmark
datasets show significant improvement using these new MKL formulations