164,611 research outputs found
Multiple Retrieval Models and Regression Models for Prior Art Search
This paper presents the system called PATATRAS (PATent and Article Tracking,
Retrieval and AnalysiS) realized for the IP track of CLEF 2009. Our approach
presents three main characteristics: 1. The usage of multiple retrieval models
(KL, Okapi) and term index definitions (lemma, phrase, concept) for the three
languages considered in the present track (English, French, German) producing
ten different sets of ranked results. 2. The merging of the different results
based on multiple regression models using an additional validation set created
from the patent collection. 3. The exploitation of patent metadata and of the
citation structures for creating restricted initial working sets of patents and
for producing a final re-ranking regression model. As we exploit specific
metadata of the patent documents and the citation relations only at the
creation of initial working sets and during the final post ranking step, our
architecture remains generic and easy to extend
Self-Paced Multi-Task Learning
In this paper, we propose a novel multi-task learning (MTL) framework, called
Self-Paced Multi-Task Learning (SPMTL). Different from previous works treating
all tasks and instances equally when training, SPMTL attempts to jointly learn
the tasks by taking into consideration the complexities of both tasks and
instances. This is inspired by the cognitive process of human brain that often
learns from the easy to the hard. We construct a compact SPMTL formulation by
proposing a new task-oriented regularizer that can jointly prioritize the tasks
and the instances. Thus it can be interpreted as a self-paced learner for MTL.
A simple yet effective algorithm is designed for optimizing the proposed
objective function. An error bound for a simplified formulation is also
analyzed theoretically. Experimental results on toy and real-world datasets
demonstrate the effectiveness of the proposed approach, compared to the
state-of-the-art methods
Efficient Learning with Partially Observed Attributes
We describe and analyze efficient algorithms for learning a linear predictor
from examples when the learner can only view a few attributes of each training
example. This is the case, for instance, in medical research, where each
patient participating in the experiment is only willing to go through a small
number of tests. Our analysis bounds the number of additional examples
sufficient to compensate for the lack of full information on each training
example. We demonstrate the efficiency of our algorithms by showing that when
running on digit recognition data, they obtain a high prediction accuracy even
when the learner gets to see only four pixels of each image.Comment: This is a full version of the paper appearing in The 27th
International Conference on Machine Learning (ICML 2010
A Comparative Study of Machine Learning Models for Tabular Data Through Challenge of Monitoring Parkinson's Disease Progression Using Voice Recordings
People with Parkinson's disease must be regularly monitored by their
physician to observe how the disease is progressing and potentially adjust
treatment plans to mitigate the symptoms. Monitoring the progression of the
disease through a voice recording captured by the patient at their own home can
make the process faster and less stressful. Using a dataset of voice recordings
of 42 people with early-stage Parkinson's disease over a time span of 6 months,
we applied multiple machine learning techniques to find a correlation between
the voice recording and the patient's motor UPDRS score. We approached this
problem using a multitude of both regression and classification techniques.
Much of this paper is dedicated to mapping the voice data to motor UPDRS scores
using regression techniques in order to obtain a more precise value for unknown
instances. Through this comparative study of variant machine learning methods,
we realized some old machine learning methods like trees outperform cutting
edge deep learning models on numerous tabular datasets.Comment: Accepted at "HIMS'20 - The 6th Int'l Conf on Health Informatics and
Medical Systems"; https://americancse.org/events/csce2020/conferences/hims2
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