738 research outputs found
Ensemble Learning of Colorectal Cancer Survival Rates
In this paper, we describe a dataset relating to cellular and physical
conditions of patients who are operated upon to remove colorectal tumours. This
data provides a unique insight into immunological status at the point of tumour
removal, tumour classification and post-operative survival. We build on
existing research on clustering and machine learning facets of this data to
demonstrate a role for an ensemble approach to highlighting patients with
clearer prognosis parameters. Results for survival prediction using 3 different
approaches are shown for a subset of the data which is most difficult to model.
The performance of each model individually is compared with subsets of the data
where some agreement is reached for multiple models. Significant improvements
in model accuracy on an unseen test set can be achieved for patients where
agreement between models is achieved.Comment: IEEE International Conference on Computational Intelligence and
Virtual Environments for Measurement Systems and Applications (CIVEMSA) 2013,
pp. 82 - 86, 201
Biomarker Clustering of Colorectal Cancer Data to Complement Clinical Classification
In this paper, we describe a dataset relating to cellular and physical
conditions of patients who are operated upon to remove colorectal tumours. This
data provides a unique insight into immunological status at the point of tumour
removal, tumour classification and post-operative survival. Attempts are made
to cluster this dataset and important subsets of it in an effort to
characterize the data and validate existing standards for tumour
classification. It is apparent from optimal clustering that existing tumour
classification is largely unrelated to immunological factors within a patient
and that there may be scope for re-evaluating treatment options and survival
estimates based on a combination of tumour physiology and patient
histochemistry.Comment: Federated Conference on Computer Science and Information Systems
(FedCSIS), pp 187-191, 201
One-Class Classification: Taxonomy of Study and Review of Techniques
One-class classification (OCC) algorithms aim to build classification models
when the negative class is either absent, poorly sampled or not well defined.
This unique situation constrains the learning of efficient classifiers by
defining class boundary just with the knowledge of positive class. The OCC
problem has been considered and applied under many research themes, such as
outlier/novelty detection and concept learning. In this paper we present a
unified view of the general problem of OCC by presenting a taxonomy of study
for OCC problems, which is based on the availability of training data,
algorithms used and the application domains applied. We further delve into each
of the categories of the proposed taxonomy and present a comprehensive
literature review of the OCC algorithms, techniques and methodologies with a
focus on their significance, limitations and applications. We conclude our
paper by discussing some open research problems in the field of OCC and present
our vision for future research.Comment: 24 pages + 11 pages of references, 8 figure
Ensemble learning of colorectal cancer survival rates
In this paper, we describe a dataset relating to cellular and physical conditions of patients who are operated upon to remove colorectal tumours. This data provides a unique insight into immunological status at the point of tumour removal, tumour classification and post-operative survival. We build on existing research on clustering and machine learning facets of this data to demonstrate a role for an ensemble approach to highlighting patients with clearer prognosis parameters. Results for survival prediction using 3 different approaches are shown for a subset of the data which is most difficult to model. The performance of each model individually is compared with subsets of the data where some agreement is reached for multiple models. Significant improvements in model accuracy on an unseen test set can be achieved for patients where agreement between models is achieved
On Sparse Modern Hopfield Model
We introduce the sparse modern Hopfield model as a sparse extension of the
modern Hopfield model. Like its dense counterpart, the sparse modern Hopfield
model equips a memory-retrieval dynamics whose one-step approximation
corresponds to the sparse attention mechanism. Theoretically, our key
contribution is a principled derivation of a closed-form sparse Hopfield energy
using the convex conjugate of the sparse entropic regularizer. Building upon
this, we derive the sparse memory retrieval dynamics from the sparse energy
function and show its one-step approximation is equivalent to the
sparse-structured attention. Importantly, we provide a sparsity-dependent
memory retrieval error bound which is provably tighter than its dense analog.
The conditions for the benefits of sparsity to arise are therefore identified
and discussed. In addition, we show that the sparse modern Hopfield model
maintains the robust theoretical properties of its dense counterpart, including
rapid fixed point convergence and exponential memory capacity. Empirically, we
use both synthetic and real-world datasets to demonstrate that the sparse
Hopfield model outperforms its dense counterpart in many situations.Comment: 37 pages, accepted to NeurIPS 202
Random Projection in Deep Neural Networks
This work investigates the ways in which deep learning methods can benefit
from random projection (RP), a classic linear dimensionality reduction method.
We focus on two areas where, as we have found, employing RP techniques can
improve deep models: training neural networks on high-dimensional data and
initialization of network parameters. Training deep neural networks (DNNs) on
sparse, high-dimensional data with no exploitable structure implies a network
architecture with an input layer that has a huge number of weights, which often
makes training infeasible. We show that this problem can be solved by
prepending the network with an input layer whose weights are initialized with
an RP matrix. We propose several modifications to the network architecture and
training regime that makes it possible to efficiently train DNNs with learnable
RP layer on data with as many as tens of millions of input features and
training examples. In comparison to the state-of-the-art methods, neural
networks with RP layer achieve competitive performance or improve the results
on several extremely high-dimensional real-world datasets. The second area
where the application of RP techniques can be beneficial for training deep
models is weight initialization. Setting the initial weights in DNNs to
elements of various RP matrices enabled us to train residual deep networks to
higher levels of performance
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