Temporal evolution of generalization during learning in linear networks

We study generalization in a simple framework of feedforward linear networks with n inputs and n outputs, trained from examples by gradient descent on the usual quadratic error function. We derive analytical results on the behavior of the validation function corresponding to the LMS error function calculated on a set of validation patterns. We show that the behavior of the validation function depends critically on the initial conditions and on the characteristics of the noise. Under certain simple assumptions, if the initial weights are sufficiently small, the validation function has a unique minimum corresponding to an optimal stopping time for training for which simple bounds can be calculated. There exists also situations where the validation function can have more complicated and somewhat unexpected behavior such as multiple local minima (at most n) of variable depth and long but finite plateau effects. Additional results and possible extensions are briefly discussed

Neural Networks for Fingerprint Recognition

After collecting a data base of fingerprint images, we design a neural network algorithm for fingerprint recognition. When presented with a pair of fingerprint images, the algorithm outputs an estimate of the probability that the two images originate from the same finger. In one experiment, the neural network is trained using a few hundred pairs of images and its performance is subsequently tested using several thousand pairs of images originated from a subset of the database corresponding to 20 individuals. The error rate currently achieved is less than 0.5%. Additional results, extensions, and possible applications are also briefly discussed

Hybrid modeling, HMM/NN architectures, and protein applications

We describe a hybrid modeling approach where the parameters of a model are calculated and modulated by another model, typically a neural network (NN), to avoid both overfitting and underfitting. We develop the approach for the case of Hidden Markov Models (HMMs), by deriving a class of hybrid HMM/NN architectures. These architectures can be trained with unified algorithms that blend HMM dynamic programming with NN backpropagation. In the case of complex data, mixtures of HMMs or modulated HMMs must be used. NNs can then be applied both to the parameters of each single HMM, and to the switching or modulation of the models, as a function of input or context. Hybrid HMM/NN architectures provide a flexible NN parameterization for the control of model structure and complexity. At the same time, they can capture distributions that, in practice, are inaccessible to single HMMs. The HMM/NN hybrid approach is tested, in its simplest form, by constructing a model of the immunoglobulin protein family. A hybrid model is trained, and a multiple alignment derived, with less than a fourth of the number of parameters used with previous single HMMs

A Connectionist Architecture for Sequential Decision Learning

a connectionist architecture and learning algorithm for sequential decision learning are presented. The architecture provides representations for probabilities and utilities. The learning algorithm provides a mechanism to learn from longterm rewards/utilities while observing information available locally in time. The mechanism is based on gradient ascent on the current estimate of the long-term reward in the weight spju^e defined by a "policy" network. The learning principle can be seen as a generalization of previous methods proposed to implement "policy iteration" mechanisms with connectionist networks. The algorithm is simulated for an "agent" moving in an environment described as a simple one-dimensional random walk. Results show the agent discovers optimal moving strategies in simple caises and learns how to avoid short-term suboptimal rewards in order to maximize long-term rewards in more complex cases

Fluorinated analogues of lipidic dialkynylcarbinol pharmacophores: synthesis and cytotoxicity in HCT116 cancer cells

Lipidic alkynylcarbinols (LACs) have been identified as potential antitumor compounds, and a thorough understanding of their pharmacophoric environment is now required to elucidate their biological mode of action. In the dialkynylcarbinol (DAC) series, a specific study of the pharmacophore potential has been undertaken by focusing on the synthesis of three fluorinated derivatives followed by their biological evaluation. This work highlights the requirement of an electron-rich secondary carbinol center as a key structure for cytotoxicity in HCT116 cells

Ethynylogation approach in antitumor lipid pharmacochemistry: from dialkynyl-carbinols to trialkynyl-carbinols

A recently proposed "ethynylogation" pharmacochemical approach, first envisaged in the series of anticancer lipidic dialkynylcarbinols (DACs) H–C≡C–CH(OH)–C≡C–R at the levels of the H–C⋮ and ⋮C–R bonds for R = n-C12H25, is completed here at the level of the (HO)C–H bond. The so-devised mono-lipidic trialkynylcarbinol (TAC) target (HC≡C)2C(OH)–C≡CR and its bis-lipidic counterpart HC≡C–C(OH)(C≡CR)2 were synthesized in 4 steps and with 33 % and 23 % overall yield, respectively. Their antitumor cytotoxicity has been evaluated towards HCT116 cells: while the latter TAC is totally inactive, the former DAC-ethynylogous TAC still exhibits a significant toxicity with an IC50 of 10 µM