Search Results for author:
Found 10 papers, 3 papers with code
On the Trade-off between the Number of Nodes and the Number of Trees in a Random Forest
We also show that a bag of $n$ decision trees can be represented by a bag of $T$ decision trees each with polynomial size if $n-T$ is a constant and a small classification error is allowed.
Molecular Design Based on Integer Programming and Splitting Data Sets by Hyperplanes
A novel framework for designing the molecular structure of chemical compounds with a desired chemical property has recently been proposed.
Molecular Design Based on Integer Programming and Quadratic Descriptors in a Two-layered Model
A set of graph theoretical descriptors in the feature function plays a key role to derive a compact formulation of such an MILP.
On the Size and Width of the Decoder of a Boolean Threshold Autoencoder
In this paper, we study the size and width of autoencoders consisting of Boolean threshold functions, where an autoencoder is a layered neural network whose structure can be viewed as consisting of an encoder, which compresses an input vector to a lower dimensional vector, and a decoder which transforms the low-dimensional vector back to the original input vector exactly (or approximately).
A Method for Inferring Polymers Based on Linear Regression and Integer Programming
For this, we introduce a new way of representing a polymer as a form of monomer and define new descriptors that feature the structure of polymers.
Molecular Design Based on Artificial Neural Networks, Integer Programming and Grid Neighbor Search
In the framework, a chemical graph with a target chemical value is inferred as a feasible solution of a mixed integer linear program that represents a prediction function and other requirements on the structure of graphs.
An Inverse QSAR Method Based on Linear Regression and Integer Programming
In the framework, we first define a feature vector $f(C)$ of a chemical graph $C$ and construct an ANN that maps $x=f(C)$ to a predicted value $\eta(x)$ of a chemical property $\pi$ to $C$.
A Novel Method for Inference of Acyclic Chemical Compounds with Bounded Branch-height Based on Artificial Neural Networks and Integer Programming
In the second phase, given a target value $y^*$ of property $\pi$, a feature vector $x^*$ is inferred by solving an MILP formulated from the trained ANN so that $\psi(x^*)$ is close to $y^*$ and then a set of chemical structures $G^*$ such that $f(G^*)= x^*$ is enumerated by a graph search algorithm.
Data Structures and Algorithms Computational Engineering, Finance, and Science 05C92, 92E10, 05C30, 68T07, 90C11, 92-04
On the Compressive Power of Boolean Threshold Autoencoders
An autoencoder is a layered neural network whose structure can be viewed as consisting of an encoder, which compresses an input vector of dimension $D$ to a vector of low dimension $d$, and a decoder which transforms the low-dimensional vector back to the original input vector (or one that is very similar).
Maximum margin classifier working in a set of strings
However, this non-one-to-one conversion involves a loss of information and makes it impossible to evaluate, using probability theory, the generalization error of a learning machine, considering that the given data to train and test the machine are strings generated according to probability laws.
